US20090131759A1 - Life sign detection and health state assessment system - Google Patents

Life sign detection and health state assessment system Download PDF

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US20090131759A1
US20090131759A1 US10595672 US59567204A US2009131759A1 US 20090131759 A1 US20090131759 A1 US 20090131759A1 US 10595672 US10595672 US 10595672 US 59567204 A US59567204 A US 59567204A US 2009131759 A1 US2009131759 A1 US 2009131759A1
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data
sensor
normal
state
health
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US10595672
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Nathaniel Sims
Nhedti Colquitt
Michael Wollowitz
Matt Hickcox
Michael Dempsey
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General Hospital Corp
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Nathaniel Sims
Nhedti Colquitt
Michael Wollowitz
Matt Hickcox
Michael Dempsey
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    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/68Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
    • A61B5/6801Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
    • A61B5/6802Sensor mounted on worn items
    • A61B5/6804Garments; Clothes
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/02Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A61B5/0205Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
    • A61B5/02055Simultaneously evaluating both cardiovascular condition and temperature
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/103Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
    • A61B5/113Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb occurring during breathing
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/103Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
    • A61B5/113Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb occurring during breathing
    • A61B5/1135Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb occurring during breathing by monitoring thoracic expansion
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/68Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
    • A61B5/6801Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
    • A61B5/683Means for maintaining contact with the body
    • A61B5/6831Straps, bands or harnesses
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/72Signal processing specially adapted for physiological signals or for diagnostic purposes
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F19/00Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/30Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
    • G06F19/34Computer-assisted medical diagnosis or treatment, e.g. computerised prescription or delivery of medication or diets, computerised local control of medical devices, medical expert systems or telemedicine
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F19/00Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/30Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
    • G06F19/34Computer-assisted medical diagnosis or treatment, e.g. computerised prescription or delivery of medication or diets, computerised local control of medical devices, medical expert systems or telemedicine
    • G06F19/3418Telemedicine, e.g. remote diagnosis, remote control of instruments or remote monitoring of patient carried devices
    • GPHYSICS
    • G16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16HHEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H15/00ICT specially adapted for medical reports, e.g. generation or transmission thereof
    • GPHYSICS
    • G16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16HHEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • GPHYSICS
    • G16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16HHEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/30ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
    • GPHYSICS
    • G16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16HHEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/80ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for detecting, monitoring or modelling epidemics or pandemics, e.g. flu
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B2562/00Details of sensors; Constructional details of sensor housings or probes; Accessories for sensors
    • A61B2562/02Details of sensors specially adapted for in-vivo measurements
    • A61B2562/0261Strain gauges

Abstract

A wearable platform embodied in a belt or patch provides physiological monitoring of soldiers during field operations or trauma victims at accident sites and makes health state assessments. The platform includes sensors for heart rate, body motion, respiration rate and intensity, and temperature and further contains a microprocessor and short range transmitter. An analog circuit running an algorithm obtains the R-wave period from the EKG signal and produces electrical pulses with the period between pulses corresponding to the R-wave period. A rule based processing engine having an evaluation algorithm is capable of making a medical evaluation of subject condition and determines a confidence level for the evaluation. The rules are subject to variation depending upon the subject population. The information is communicated wirelessly to a local hub for relay to a remote monitor.

Description

  • [0001]
    This patent claims the benefit of the priority of Provisional Patent Application 60/517,149, filed Nov. 4, 2003 and entitled “Life Sign Detection and Health State Assessment System”, the full disclosure of which is incorporated by reference.
  • FIELD OF THE INVENTION
  • [0002]
    This invention relates to compact wearable systems for measuring a subject's vital signs, such as heart rate, respiration rate, temperature, position and motion, possibly processing those measurements and transmitting the results of the processing wirelessly for remote monitoring. It also relates to processing algorithms for generating electrical signals indicating vital signs, and more generally for developing diagnostic information from groups of sensor readings.
  • BACKGROUND OF THE INVENTION
  • [0003]
    Electronic devices for monitoring a patient's vital signs at bedside are in common use in hospitals. Typically these measure, display and transmit to nursing stations EKG traces, blood pressure values, body temperature values, respiration rates and other vitals. To accomplish this, sensors such as EKG pads, pressure cuffs, thermometers, etc. are attached to the patient by multiple leads. Each of the different devices is designed for use on a patient with limited mobility in a stable environment. In addition there are monitoring devices for heart rate, respiration and body temperature, designed for use by athletes, pilots, astronauts, etc. Some of the systems employ wireless transmission to a monitor. Again, these devices are designed for use in predictable environmental situations often where low cost and low transmission bandwidth are not limiting factors. These devices for the most part report detailed data such as full EKG trace details, although some merely have alarm functions if certain parameters are exceeded.
  • [0004]
    There has long been desired a reliable, reasonably low cost system for monitoring and aiding in the triaging of wounded soldiers in a battlefield environment, or triaging multiple trauma victims at an accident site. In particular the devices do not exist that could be worn by a soldier in the chaotic battlefield environment to provide enough useful information on vitals to say with confidence that the soldier is beyond the point where medical intervention would be useful, so as to be able to avoid or terminate rescue attempts that place a rescuer's life in jeopardy. Thus medics and other rescue personnel continue to be killed or seriously injured attempting to rescue soldiers where rescue was already hopeless. Despite the great need for systems to avoid such unnecessary casualties there has heretofore been no satisfactory system economical enough to simultaneously monitor large numbers of armed forces.
  • [0005]
    Such a system would also obviously be useful in non-military chaotic situations. For example ambulances and emergency vehicles are often equipped with personnel and diagnostic equipment that can be overwhelmed in situations where there are several injured to treat simultaneously. Again, there is not available at reasonable cost health assessment systems that could be applied to multiple subjects to allow triaging to take place rapidly by persons remote from the scene of injury.
  • [0006]
    Suggestions have been made as to how such systems should be organized and what should be measured. Nevertheless, despite these suggestions and the great need, the prior art has not advanced to the point where such systems have been built and made practical.
  • [0007]
    Prior patents that may relate to the problems of health state assessment are the following:
  • Respiration Effort Sensors
  • [0008]
    U.S. Pat. No. 5,513,646 entitled “Personal Security Monitoring System and Method” discloses a breath detector and a signal processor where the signal processor distinguishes between the user's normal breathing patterns and other patterns that trigger alarms.
  • [0009]
    U.S. Pat. No. 5,611,349 entitled “Respiration Monitor with Simplified Breath Detector” discloses a pneumatic breath detector using a low pass filter to reduce signals not indicative of respiration.
  • [0010]
    U.S. Pat. No. 6,377,185 entitled “Apparatus and Method for Reducing Power Consumption in Physiological Condition Monitors” discloses using a high power mode when data is written and a low power mode when inactive. Incoming data is placed in a low power buffer and transferred in a single data transfer.
  • [0011]
    U.S. Pat. No. 5,331,968 entitled “Inductive Plethysmographic Transducers and Electronic Circuitry Therefor” discloses such a device where the circuitry is located remotely rather than on a transducer.
  • [0012]
    U.S. patent application 2002/0032386 entitled “Systems and Methods for Ambulatory Monitoring of Physiological Signs” discloses monitoring apparel with attached sensors for pulmonary and cardiac function by including ECG leads and a plethysmographic sensor. Data from the sensors is stored in a computer readable medium for later use by health care providers.
  • Sensors and Signal Processors for Extracting a Physiological Measurement, Especially in a High Noise or High Motion Environment
  • [0013]
    U.S. Pat. No. 6,520,918 entitled “Method and Device for Measuring Systolic and Diastolic Blood Pressure and Heart Rate in an Environment with Extreme Levels of Noise and Vibrations” discloses using an acoustic sensor on the patient near an artery and a second acoustic transducer away from the artery. The signal of the first sensor is processed using an adaptive interfere canceller algorithm with the signal of the second acoustic sensor as interferer.
  • [0014]
    U.S. Pat. No. 6,629,937 entitled “System for Processing Audio, Video and Other Data for Medical Diagnosis and Other Applications” discloses a system for storing acoustic data in a file associated with a patient's medical record, which are analyzed to determine physiologically significant features useful in medical diagnosis based on an automatic analysis.
  • [0015]
    U.S. Pat. No. 5,853,005 entitled “Acoustic Monitoring System” discloses a transducer that monitors acoustic signals representative of heartbeat or breathing and transferred into a fluid. A comparison is made with predetermined reference patters.
  • [0016]
    U.S. Pat. No. 6,616,613 entitled “Physiological Signal Monitoring System” discloses a system for determining blood pressure, heart rate, temperature, respiratory rate, and arterial compliance on the basis of signal characteristics of the systolic wave pulse. The systolic reflected wave pulse contour is subtracted from the digital volume pulse contour.
  • [0017]
    U.S. Pat. No. 6,200,270 entitled “Sensor for Non-Invasive and Continuous Determination of the Duration of Arterial Pulse Waves” discloses two spaced apart piezoelectric pressure sensors along the artery.
  • Wireless Networks—Low Power Digital Data Networks in the “Body Area” or “Personal Area” Space; Selectable Data Rates, Data Buffering and Store and Forward Means
  • [0018]
    U.S. Pat. No. 6,577,893 entitled “Wireless Medical Diagnosis and Monitoring Equipment” discloses wireless electrodes attached to the surface of the skin of a patient and having a digital transmitting and receiving unit.
  • [0019]
    U.S. Pat. No. 5,755,230 entitled “Wireless EEG System for Effective Auditory Evoked Response” discloses an electrode array for attachment to a person that senses voltages produced by brain electrical activity. An operator interface records a verbal stimulus and provides a comparison of the brain activity with the stimulus.
  • [0020]
    U.S. Pat. No. 6,167,258 entitled “Programmable Wireless Data Acquisition System” discloses such a data collecting system where a transmitting device can receive multiple inputs and transmit a signal encoded with data corresponding to the inputs.
  • [0021]
    U.S. Pat. No. 6,223,061 entitled “Apparatus for Low Power Radio Communications” discloses such a system controlled frequency modulation where a phase lock loop synthesizer is set to an open loop state to allow FM unimpeded by the normal frequency correcting action of the synthesizer.
  • [0022]
    U.S. patent application 2002/0091785 entitled “Intelligent Data Network” provides two-way communication between a node and a master device by pausing to listen after each transmission.
  • [0023]
    U.S. Pat. No. 6,450,953 entitled “Portable Signal Transfer Unit” discloses a system for relaying physiological data employing a memory for buffering the signal and wirelessly transmitting it to a remote unit.
  • [0024]
    U.S. Pat. No. 6,454,708 entitled “Portable Remote Patient Telemonitoring System Using a Memory Card or Smart Card” discloses a system that records full waveform data on smart cards. The system uses cordless, disposable sensors.
  • Systems for Remote Monitoring of Personnel
  • [0025]
    U.S. Pat. No. 6,579,231 entitled “Personal Medical Monitoring Unit and System” discloses a portable unit worn by a patient that stores physiological data and issues medical alarm conditions via wireless communications. The unit works with a central reporting system for long term collection and storage of the subjects data and can automatically dispense chemicals.
  • [0026]
    U.S. patent application 2002/0019586 entitled “Apparatus for Monitoring Health, Wellness and Fitness” discloses two sensors coupled to a processor and a memory for storing the data, which is subsequently transmitted.
  • [0027]
    U.S. Pat. No. 6,605,038 entitled “System for Monitoring Health, Wellness and Fitness” discloses a sensor worn on the upper arm including an accelerometer, GSR sensor and heat flux sensor. A central monitoring unit generates analytical status data that is transmitted to a recipient.
  • [0028]
    U.S. Pat. No. 6,160,478 entitled “Wireless Health Monitoring System” discloses a system for remotely monitoring a person's physical activity through use of an accelerometer. It may be used to determine whether a person has fallen and the likely severity of the fall and trigger an alarm.
  • [0029]
    U.S. Pat. No. 6,611,206 entitled “Automatic System for Monitoring Independent Person Requiring Occasional Assistance” discloses monitoring independent signals and combining them into a single alarm for possible intervention by a supervisor.
  • [0030]
    U.S. Pat. No. 6,198,394 entitled “System for Remote Monitoring of Personnel” discloses sensors disposable on a soldier that communicate with a soldier unit that can process the information to ensure that it falls within acceptable ranges and communicate with remote monitors. Body surface and ambient temperature are monitored. The information may be stored and kept with the soldier to enable improved care as the soldier is moved to higher levels of care.
  • [0031]
    U.S. Pat. No. 6,433,690 entitled “Elderly Fall Monitoring Method and Device” discloses a system for recording acceleration and body position data from elderly or disabled persons. It detects health and life threatening falls and notifies nursing personnel of the need for assistance.
  • [0032]
    U.S. Pat. No. 6,416,471 entitled “Portable Remote Patient Telemonitoring System” discloses a system for transferring the full waveform ECG, full waveform respiration, skin temperature and motion data to a transfer unit worn by the patient on a belt for subsequent transfer to a monitoring base station where clinical data can be compared against given profiles.
  • [0033]
    U.S. Pat. No. 6,559,620 entitled “System and Method for Remote Monitoring Utilizing a Rechargeable Battery” discloses using such a battery in a system for remotely monitoring a person's position by GPS satellite.
  • Wearable Physiological Sensor Arrays and Processing Means Therefor (Vests, Straps, Adhesive Arrays, Etc.)
  • [0034]
    U.S. Pat. No. 6,527,711 entitled “Wearable Human Physiological Data Sensors and Reporting System Therefor” discloses a series of rigid and flexible pods within which sensors and computing apparatus are housed. The system allows relative movement of the rigid sections with respect to each other.
  • [0035]
    U.S. Pat. No. 6,494,829 entitled “Physiological Sensor Array” discloses a system for transmitting sensor output. Respiration is detected by a bend sensor.
  • [0036]
    U.S. patent application 2003/0105403 entitled “Wireless ECG System” discloses a cardiac monitor for a patient that transmits signals digitally to a remote base station, which converts the signals back to analog electrical signals to be read by an ECG monitor.
  • Sensors for Use in Physiological Monitoring (Temperature, Body Positions Blood Pressure, EKG or Heart Rate), Especially Under Exercise Conditions
  • [0037]
    U.S. Pat. No. 5,168,874 entitled “Wireless Electrode Structure for Use in Patient Monitoring System” discloses a wireless patient monitoring system using a patch electrode having a micro-chip amplifier on one side of the patch electrode.
  • [0038]
    U.S. Pat. No. 5,622,180 entitled “Device for Measuring Heartbeat Rate” discloses a wrist strap with skin contact electrodes such that signals from a skin sensor are filtered and pulse shaped for display.
  • [0039]
    U.S. Pat. No. 5,976,083 entitled “Portable Aerobic Fitness Monitor for Walking and Running” discloses a system for calculating the fitness of a person using personal data and comparing that data to pedometer and heart rate values generated during exercise.
  • [0040]
    U.S. Pat. No. 4,566,461 entitled “Health Fitness Monitor” discloses a heart rate monitor for use in aerobic exercise that calculates a fitness parameter by monitoring heart rate as the subject paces through an exercise stress test protocol. The system emits beeps that the subject matches to its stride frequency. At the point of exhaustion the maximal oxygen uptake capacity is determined and is displayed.
  • [0041]
    U.S. Pat. No. 5,544,661 entitled “Real Time Ambulatory Patient Monitor” discloses a patient monitoring system including an ECG and a photo-plethysmograph, arrhythmia analysis apparatus and an expert system for determining if a pre-established critical parameter set has been exceeded. When alarmed the ECG waveform and trends are transmitted to a clinician.
  • [0042]
    U.S. Pat. No. 6,236,882 entitled “Noise Rejection for Monitoring ECGs” discloses a looping memory for storing triggered physiologic events (such as arrhythmias and syncopal events) with auto triggers to record the ECGs. Denial and extensible accommodation periods are introduced in the R-wave sensing registration for triggering data storage.
  • [0043]
    U.S. Pat. No. 5,743,269 entitled “Cardiotachometer” discloses a system for computing a heart rate from ECG signals and encoding the signals for transmission to avoid erroneous reception of signals generated by noise or interference.
  • BRIEF DESCRIPTION OF THE INVENTION
  • [0044]
    A system makes health state assessments based on data from a wearable platform embodied in a belt or patch that provides physiological monitoring of soldiers during field operations or trauma victims at accident sites. The system is the first capable of making a determination of the health state of the wearer with sufficient confidence to base triage decisions on that determination as opposed to merely reporting vital sign data. The system, in addition to sensors of vital signs and telemetry, has a rule processing engine, comprising a microprocessor running a health state assessment algorithm. It makes a health state assessment based on the remarkable determination that for an individual who is monitored in real time for at least one significant vital sign (e.g., heart rate, ECG waveforms, SpO2, respiration rate) and possibly one or more indirect life signs (such as body movement or response to direct command), it is possible to determine, with reasonable accuracy, whether the person is alive or dead. With an appropriately rich set of direct and indirect life signs, it is possible to further estimate the likelihood of injury or even the nature of an injury for such applications as remote triage.
  • [0045]
    The system makes a health state assessment of a subject at a location remote from a clinician based on indications of vital signs together with a simulation of an on site assessment of the subject. In place of the visual observations that would be part of an on site assessment of the subject, sensors provide indications of indirect life signs such as movement, orientation and position. These indications of vital signs and indirect life signs are input to rule sets implemented in a rule processing engine executing a health state assessment algorithm.
  • [0046]
    The rule sets may be varied depending upon the general characteristics of the subject population. Such populations may range from healthy young soldiers to elderly overweight individuals. The rule sets may be changed at different levels. At the highest level, typically attended by a clinician, the changes may comprise the inclusion or removal of parameters for particular indications. At a lower level of clinical support, the changes may comprise changing the range or interpretation afforded the values of the different parameters. The system accommodates parameter values that don=t correspond to numeric values, such as whether a motion fits the categories of slow, medium or fast, or indicates that a shock has occurred outside the scale of any such motion. The various rule sets may be achieved by simulating the experience of skilled health professionals.
  • [0047]
    A feature of the invention is the integration achieved between the various components. The respiration sensor for example may sense abdominal motion and thus also gives information on motion of the subject, which supplements the information provided by an accelerometer sensor. Thus there is a synergistic relation among the various sensor components and with processor elements. All of the sensor information is assimilated by a health state assessment algorithm (HSAA) that is capable of making a medical evaluation of a subject's condition and determining a confidence level for the evaluation.
  • [0048]
    The system of the invention is referred to as the Life Signs Detection System (LSDS) since one of its functions is to determine with confidence whether a warrior is alive. The wearable platform preferably includes sensors for heart rate, body motion, respiration rate and respiration intensity, and temperature and further contains a microprocessor and short range transmitter. A separate wearable package that would be expected to be carried by a soldier for other communication purposes contains a local transceiver hub that receives signals from the short range transmitter and transmits the signals more remotely.
  • [0049]
    Data received from the various sensors are processed in a microprocessor to produce a simplified, low-bandwidth output. The output is transmitted from the wearable package by a short range RF transmitter contained within the unit.
  • [0050]
    An additional component, called the Local Hub, is also worn by the subject. In its simplest form the local hub contains a short range RF transceiver, a medium or long range RF transmitter or transceiver, and a microprocessor. The local hub receives the transmitted data from the LSDS wearable package and retransmits the signals to a remote station or base station. Retransmission is not necessarily synchronous with reception; the microprocessor may perform additional processing on the received data, may store the received data for later transmission, may add information to the data, and may reconfigure the data for more efficient transmission or other reasons (e.g. increased security or privacy).
  • [0051]
    A sensor subsystem is responsible for conversion of one or more hardware biologic indicators into a periodic digital data packet. This data packet will be transmitted over a local, low-power RF link to the hub, at an appropriate data rate. Alternately, but less preferably, the sensors could rely on A/D conversion in the hub.
  • [0052]
    A hub subsystem is responsible for collection of all the local sensor data, performing additional data analysis if needed, and relaying the information to the remote station. The hub subsystem is responsible for recognizing and maintaining association to a specific set of sensor subsystems, so that data from other sensors that are physically proximate, but are monitoring a different person will not get mixed in. The hub subsystem is responsible for providing periodic and/or on-demand advertisement of its availability and status, and to accept a connection from one or more external display or other systems.
  • [0053]
    A remote subsystem is responsible for collecting data from multiple hubs, for example up to 20 hubs, and displaying them on a normal-sized laptop or portable computer screen.
  • [0054]
    A medic PDA subsystem used in lieu of or in addition to the remote station is responsible for providing the detailed data display for a selected hub.
  • [0055]
    Processing of signals takes place at various levels within the electronics worn by the subject. The levels are:
      • 1. Original Signal—The raw, unprocessed signal generated by a sensing element.
      • 2. Preparation—Basic analog processing, including amplification, applied to the signal to make it usable for later processes.
      • 3. Storage (optional)—Retention of digital values for possible later use.
      • 4. Feature Extraction—Analog and/or digital processing of the signal to obtain recognition of basic signal features such as frequency and amplitude.
      • 5. Scoring—Digital processing to determine metrics of extracted features such as averages, trends, and bin (level) counts.
      • 6. Evaluation—Digital processing of data to determine overall conditions, access whether data is within normal ranges, and to generate warnings or alarms.
      • 7. Extended Evaluation—Intensive digital processing to correlate multiple signals or multiple subjects, access the quality of received data and signals, and to perform complex feature extraction.
  • [0063]
    The invention thus provides a health state assessment rule processing engine, comprised of algorithms that estimate physiologic state and decision confidence by applying one or more medical determination “rule sets” to data received from the sensor array and from any clinician input devices in the system. Medical determination rule sets consist of decision logic and related parameter limit ranges tailored to a subject's health category. Examples of health categories include “healthy adult”, “Congestive Heart Failure (CHF) patient”, and “subject's personal health baseline.” The default rule set for the algorithm is the healthy adult category. Data from clinician input devices is optional, and consists of information observed on-site, such as “ballistic injury to limb.”
  • [0064]
    The assessment of physiologic state may be limited to good/weak/poor determinations of health given the default sensor array that detects heart rate, respiration rate, activity/orientation, and temperature. With an extended sensor array (for example, by adding blood pressure and oximetry), the assessment may be as comprehensive as normal/needs attention/critical determinations of health, along with continuous “remote triage” indicators (such as “high likelihood of shock”).
  • [0065]
    Other objects of the invention will be apparent from the following detailed description of the invention.
  • BRIEF DESCRIPTION OF THE DRAWINGS
  • [0066]
    FIG. 1 is a drawing of the LSDS worn by its subject.
  • [0067]
    FIG. 2 is a perspective drawing of the central housing and extensions viewed from the back side.
  • [0068]
    FIG. 3 is a perspective drawing of the central housing and extensions view from the front.
  • [0069]
    FIG. 4 is a cut away drawing of the extension and flex sensor.
  • [0070]
    FIG. 5 is a drawing of the patch embodiment of the invention worn by its subject.
  • [0071]
    FIG. 6 is a drawing of a flex transducer for a bending sensor mode.
  • [0072]
    FIG. 7 is a drawing of a the flex transducer for a bending sensor flexed downwards.
  • [0073]
    FIG. 8 is a drawing of a the flex transducer for a bending sensor flexed upwards.
  • [0074]
    FIG. 9 is a drawing of a flex sensor element.
  • [0075]
    FIG. 10 is a drawing of a flex sensor mounted to a support.
  • [0076]
    FIG. 11 is a drawing of a flex sensor and support.
  • [0077]
    FIG. 12 is a drawing of another flex sensor and support.
  • [0078]
    FIG. 13 is block diagram of the electronics of the LSDS.
  • [0079]
    FIG. 14 is the circuit diagram for the on board processor and power control
  • [0080]
    FIG. 15 is the circuit diagram for the ECG front end circuitry.
  • [0081]
    FIG. 16 is the circuit diagram for the accelerometer and RF circuit.
  • [0082]
    FIG. 17 is the circuit diagram for the respiration circuitry.
  • [0083]
    FIG. 18 is a schematic representation of the power management scheme.
  • [0084]
    FIG. 19 is a block diagram of the major tasks of the central task manager.
  • [0085]
    FIG. 20 is a block diagram of the Heart Rate Calculation algorithm.
  • [0086]
    FIG. 21 is a block diagram of the process timing.
  • [0087]
    FIG. 22 is a flow chart of the ECG pulse detection interrupt circuit.
  • [0088]
    FIG. 23 is a flowchart of the low pass filter and noise cancellation circuit.
  • [0089]
    FIG. 24 is an example of pulses filtered for R-waves.
  • [0090]
    FIG. 25 is an example of pulses analyzed for consistent inter-beat intervals.
  • [0091]
    FIG. 26 is a flow chart of the trend-acquiring process.
  • [0092]
    FIG. 27 is a diagrams of R-wave pulses found when tracking an existing trend.
  • [0093]
    FIG. 28 is a flow chart of the trend-tracking process
  • [0094]
    FIG. 29 is a chart showing the sample averaging scenario.
  • [0095]
    FIG. 30 is and operational overview of the communication to a serial port.
  • [0096]
    FIG. 31 is an example of a bit stream.
  • [0097]
    FIG. 32 is and example of a bit stream leader with all zeroes.
  • [0098]
    FIG. 33 is a diagram so show how orientation is interpreted.
  • [0099]
    FIG. 34 is Table 1: LSDS Platform Parameters and Error Conditions
  • [0100]
    FIG. 35 is Table 2: Default Health State Classification Descriptions
  • [0101]
    FIG. 36 is Table 3: Default Life Signs Interpretation Rules for Alive/Green and Dead/Red States
  • [0102]
    FIG. 37 is Table 4: Default Life Signs Interpretation Rules for Alive/Yellow State
  • [0103]
    FIG. 38 is Table 5: Default LSDS Alive/Normal Data Ranges
  • [0104]
    FIG. 39 is Table 6: Default LSDS Alive/Not-Normal Data Ranges
  • [0105]
    FIG. 40 is Table 8: Default Decision Matrix for Only One Parameter in Last Decision Interval
  • [0106]
    FIG. 41 is Table 9: Default Decision Matrix for Two Parameter Over Last 16 Decision Interval
  • [0107]
    FIG. 42 is Table 10: Default Decision Matrix for Three Parameters in Last Decision Interval
  • [0108]
    FIG. 43 is Table 11: Decision Matrix for Four Parameters in Last Decision Interval
  • [0109]
    FIG. 44 is Table 12: State Change Score Components
  • [0110]
    FIG. 45 is Table 13: Persistence Score Components
  • [0111]
    FIG. 46 is Table 14: Components of Weight (Multiplier) by Parameter Set
  • [0112]
    FIG. 47 is Table 15: Confidence Score Ranges
  • [0113]
    FIG. 48 is a Block Diagram of the Life Signs Detection System
  • DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS OF THE INVENTION
  • [0114]
    This invention utilizes components and subsystems further described in copending patent application Ser. No. ______ filed on the same date as this application and assigned to the same assignee. The disclosure of that application is incorporated by reference.
  • [0115]
    As shown in FIG. 1, the Life Signs Detection System (LSDS) comprises an apparatus containing a group of sensors for certain vital physical parameters of a subject person and electronics to receive and interpret electrical signals from the sensors, process the signals and transmit information on the physical status of the subject. The group of sensors and electronics is embodied in a carrier 1 arranged to be worn by the subject. The electronics residing on a PC board is designed to accomplish most signal processing at the location of the subject and to avoid the need for robust networking and centralized computing that require large amounts of bandwidth to transmit raw signals for analysis. Such large bandwidth is impractical in field settings where bandwidth is low, unreliable and localized responsiveness must be maintained.
  • Health State Assessment Algorithm
  • [0116]
    The clinical health state assessment algorithm for use in the LSDS combines raw data collected by the LSDS sensors to determine the following information:
      • Physiologic State—Physiological data acquired and analyzed to classify general wellness and rule out a physiologic state consistent with death
      • Decision Confidence Score—A calculated indicator of diagnostic certainty of the physiologic state classification
      • Multi-Tier Processing—Using a revisable rule set, physiologic state and confidence score are calculated using a rules set based on one's personal baseline
      • Triage Indicators—Acquire, analyze, and report appropriate collection of life sign parameters to support specific diagnostic assessments.
  • [0121]
    The rules processing engine makes continuous health state assessments based on the multiplicity of current and trended sensor inputs and clinician inputs, as defined by the rule sets in use. Table 1 lists the default data available from the monitor's sensors for use in the processing engine, along with associated error conditions. A medical determination rule set for the processing engine must include the medical decision logic to produce the following minimum information based on continuous processing of raw sensor data:
      • A color-coded health state classification (green for alive and nomal, yellow for alive and not normal, red for critical or dead)
      • An indicator of extent of confidence in the accuracy of the health state classification (i.e., “diagnostic certainty”).
  • [0124]
    At least one rule set (the “primary rule set”) must be available to the rules processing engine. If more than one rule set is available, multi-tier processing can occur. This is particularly desirable when more than one type of decision must be made for the monitored patient. For example, for a soldier monitored on the battlefield the primary rule set allows remote decision-makers to determine that a soldier is alive but injured, and that a medic needs to be dispatched. A second tier of decisions would be useful to aid the dispatched medic in preparing to treat the injured soldier (for example, which medication and equipment are indicated). A rule set that is not “primary” can be configured to be continuously “active” or to be activated and deactivated on demand from a remote clinician input device.
  • [0125]
    Data trending for the rules processing engine is performed continuously for each parameter in the sensor array and for any numeric parameter provided via the clinician input device. A parameter trend is predefined (e.g., an average, a minimum or a maximum) for a fixed time interval (“decision interval”). The decision interval is based on the minimum amount of time a clinician would observe a patient's real-time physiological data before making a clinical decision based on that parameter. The default decision interval for the rules processing engine is sixteen (16) seconds. Thus with each new set of inputs from the sensor array (the default is once every two seconds), the monitored data is trended for the most recent sixteen (16) seconds (that is, the current two (2) seconds of data and the previous fourteen (14) seconds of data).
  • Health State Classifications
  • [0126]
    Table 2 defines the default health state classifications (e.g. alive, abnormal, dead, uncertain) and associates them with a color code that will be available to remote or on-site clinicians. That is, the color code will be displayed at remote assessment stations and on portable clinician input/display devices (such as PDAs).
  • Confidence Score
  • [0127]
    The confidence score represents level of confidence in the accuracy of a given health state decision. The calculation for confidence score is based on the probability (percentage of likelihood) that a given health state assessment (Green color code, Yellow color code, or Red color code) is accurate.
  • Life Signs Data Prioritization
  • [0128]
    Data prioritization is used to determine the order for proceeding through the processing engine's data interpretation rules. Prioritization provides an abstraction of the order in which an on-site clinician would examine life signs data. Prioritization also allows a “value level” to be assigned to individual life signs (from “high value” to “low value”) as an indication of usefulness in determining health state. The value level is considered in computing a confidence score for each health state assessment decision.
  • [0129]
    The default LSDS data prioritizations are as follows, based on clinical usefulness of each parameter compared to the traditional vital signs monitoring scenario, which includes visual patient inspections.
      • 1. Heart Rate—Numerical, traditional measurement of heartbeat; high value
      • 2. Respiration Rate—Numerical, traditional measurement of breathing; high value
      • 3. Speed of motion (None, Slow, Medium, or Fast), substitutes for visual observation of activity (e.g., Fast might be assumed to equal running, and can be used to justify high heart rate); medium value
      • 4. External Body Temp—Skin temperature influenced by ambient temperature (weather, garments, etc.); correlation to core temperature (traditional measurement). Skin temp can be useful in remote assessment situation where the monitored individual's environmental conditions are known to assessment personnel (or post-processing software). Low value.
      • 5. Position of body—substitutes for visual observation of standing or lying down; (e.g., sleeping could result in position of lying down, along with slowed respiration rate and speed of motion at NONE); low value
    Interpretation Rules for “Green” and “Red” Health States
  • [0135]
    This section lists the default interpretation rules to support the decisions for the Alive/Green-Normal and Dead/Red states. The Alive/Yellow-Not-Normal state and the Uncertain/Blue states are addressed in separate sections below. Each rule is stated in terms of specific life sign parameters (breadth of data) and length of observation time (duration of monitoring).
  • [0136]
    Table 3 is based on medical judgment for the default “healthy adult” population and summarizes the rules implemented in the processing engine that apply to current Alive/Green and Dead/Red states.
  • Interpretation Rules for Alive/Yellow State
  • [0137]
    Given the default life signs data available from the LSDS sensor platform, the Alive/Yellow-Not-Normal state indicates that at least one “high value” parameter, either HR or RR, is outside the normal range for a sustained time interval. Table 4 lists the default interpretation rules that support the decisions for the Alive/Yellow-Not-Normal state. Again, this is based on medical judgment for a “healthy adult” population.
  • Interpretation Rules for Uncertain/Blue State
  • [0138]
    The default Uncertain/Blue color code state is defined by all combinations of parameter values not covered in Tables 3 and 4.
  • Algorithm Boundary Conditions
  • [0139]
    This section describes the default boundary conditions applied to the LSDS health state assessment algorithm for the “healthy adult” population.
  • “Normal” Data Ranges
  • [0140]
    Table 5 lists the data ranges that define the highest and lowest values each default LSDS sensor can calculate (see Data Description column) and the default “Normal” data ranges for the “healthy adult” population.
  • Abnormal High and Abnormal Low Data Ranges
  • [0141]
    For the LDSD sensor platform, the default ALIVE/YELLOW state is characterized
  • [0000]
    by the following conditions persisting over an appropriate time interval:
      • HR is outside of normal range (non-zero, higher or lower than normal range)
      • RR is outside of normal range (non-zero, higher or lower than normal range)
      • “History” data (trend) indicates a rate of change of physiological state that correlates with certain know injury conditions (e.g., exsanguination)
      • Absolute external temperature is not below 36.4° C. and not above 38.9° C.
  • [0146]
    The resulting abnormal data value ranges for heart rate (HR), respiration rate (RR) and
  • [0000]
    skin temperature are shown in Table 6.
  • Algorithm Details
  • [0147]
    The algorithm involves two to three sequential steps that are repeated for each decision interval (a default of the most recent 16 seconds, in the case of the LSDS sensor platform), each time a new data packet is received (every two seconds, in the case of the LSDS monitor), and for each tier of processing in use. The steps are as follows:
      • Translate sensor data and any clinician inputs into health state decisions—Based on the valid sensor data, clinician inputs received in the most recent decision interval, and resulting trended data, find the appropriate decision rule and corresponding implied health state color code for each active rule set.
      • Estimate the “goodness” of the new decision—Based on the likelihood of the new state transition (previous state compared to new state), the persistence in the new state and the number and priority of parameters received for the new state, produce an appropriate confidence score.
      • Optionally, identify specific triage indicators—If an extended sensor array is available, including at least a blood pressure measurement, find and report specific triage characteristics that indicate abnormal or critical health states.
  • [0151]
    Note that the multi-tier nature or the algorithm lies in its ability to apply multiple rule sets continuously, or on demand (from a remote assessment device). Additional rule sets, such as one's own personal health baseline rules, can be processed in the on-body monitoring device or on the remote assessment device.
  • [0152]
    Processing of triage indicators relies on a triage-specific rule set. Thus the rules processing engine functions the same for health state and triage indicators. The difference is in that triage reporting includes literal “indicators” and explicit related parameter values, whereas, health state is reported as a color code.
  • (Table 7 Intentionally Omitted) Translate Sensor Data to a Health State Decision
  • [0153]
    The default rules set for the LSDS monitor have been restated in decision matrices (Tables 8-11) that correspond to the number of parameters received in a decision interval. Note that, although the default rules set primarily uses 8 seconds of sensor data for state determination, the algorithm defaults to a 16-second decision interval. This is because the physicians' rules for a assessing healthy adults are based on the availability of continuous physiological data in traditional monitors, typically eight or more data samples at 1-second intervals. The algorithm's default 16-second interval is an effort to increase the likelihood of having at least 8 data samples from the LSDS monitor, which delivers data at 2-second intervals.
  • Red State Exceptions
  • [0154]
    For the default rule set these critical red states exceptions take priority over all other state decisions, regardless of the presence of other valid parameters:
      • If HR=0 for 4 minutes or more, New state=RED, and Confidence Score=100
      • HR<30 BPM for 10 minutes or more, New state=RED, and Confidence Score=100
      • RR=0 for 5 minutes or more, New state=RED, and Confidence Score=100
    Confidence Score
  • [0158]
    The confidence score is a value of 100 or less rounded to one decimal place, with 100 as the highest possible confidence score. A score below 50 indicates a low confidence level, from 50 to below 80 indicates a medium confidence level, and 80 or above indicates a high confidence level. This is summarized in Table 15. The score is calculated as follows:
  • [0000]

    Confidence score=10*[parameter set weight*(state change score+persistence score)]
  • [0000]
    There are three components to the confidence score:
      • State change score—This score reflects the likelihood of the observed state change
      • State persistence score—The number of times the new state was previously observed in sequence
      • Parameter set weight—A multiplier intended to reflect the breadth of the most recent set of available parameters
    State Change Score
  • [0162]
    State change score is a reflection of the likelihood of going from one health state to another. The underlying probabilities are based on the following assumptions:
      • Vital signs activity tends to stabilize after a sustained period in a given level of activity for normal healthy adults
      • The sensor platform captures data quickly enough to expect health state changes to typically occur in no more than one step at a time
      • State changes of two or more steps are likely to reflect critical wounding. In the absence of actual data for likelihood of a soldier being wounded, a rough estimate was used to determine that probability
        Table 12 describes the components of the state change score.
    State Persistence Score
  • [0166]
    The confidence score factors in the amount of time that the health state does not change. As stated earlier, vital signs activity tends to stabilize after a sustained period in a given level of activity for normal healthy adults. Therefore, the algorithm assumes that persistence of a green, red, or yellow state improves the likelihood that the sensor data and resulting health state assessment are correct.
  • [0167]
    Persistence reflects number of times the current data was previously observed during the decision interval (default of the most recent 16 seconds, 8 data samples). Thus the maximum value for persistence score is 7, and the minimum value is 0.
  • [0168]
    Table 13 describes the component of the persistence score. Table 14 relates the persistence score to High, Low, and Medium influence of state persistence on the overall confidence score.
  • Parameter Set Weight
  • [0169]
    The parameter set weight is an indicator of the number and importance of the parameters used to make the health state assessment. Table 14 describes the components of the parameter set weight.
  • Description of the Physical Carrier
  • [0170]
    The carrier comprises three main elements—a central housing 3, two flexible extensions 5 containing external sensors 7 (see FIG. 2), and a harness 9. The LSDS package is intended to be won underneath the subject's clothing with the housing positioned approximately over the solar plexus. It is held in place by an elastic harness that consists of one strap (belt) that passes around the subject's back and another that passes over the left shoulder. The two flexible extensions 5 protrude from the sides of the housing and form the connections to the horizontal strap 11 of the harness.
  • Respiration Sensors
  • [0171]
    Respiration sensors 13 (FIG. 4) are used in connection with an electronic circuit to provide a signal indicative of body motions accompanying respiration. They comprise a strip or flexible film material 15 that is overprinted with conductive leads 17 connecting to a small (millimeter dimensioned) area of resistive material having the property that its resistance increases as the strip is flexed convexly. The sensor is laminated using a thermal adhesive to a thicker base layer 21. The two are then thermoformed so that the center of the strip (containing the small resistive area) is shaped into an arch 23 while the ends 25 remain flat. Small rectangles of fabric are mounted to the flat ends using a thermal adhesive; this provides a means for sewing the sensor securely to the housing extensions. Grommets or rivets are added to the sensor so that wires can be soldered in place to connect to the PC board.
  • [0172]
    Each respiration sensor is sewn to the front surface of one of the housing extensions 5, aligned along the extension. The fabric of the extension is pushed together slightly under the arched section so that the tension load when worn will be mainly across the sensor. The nylon cover material 27 is split so that the center of the sensor is uncovered, both to make it visible and to allow for greater compliance. The term “compliant” is used here to mean elastically deformable or spring-like, as opposed to the extremes of either rigid or completely flexible.
  • [0173]
    FIG. 1 shows the manner in which the configuration here described is worn be the subject. The complete assembly of central housing and extensions 3 is attached at both ends to an elastic strap that wraps tightly around the subject's back, holding the components tightly against the skin and placing a tensile load across the respiration sensor. An optional shoulder strap 9 prevents the assembly from slipping down during physical activity. The assembly is preferably placed in a horizontal alignment below the lower edge of the pectoral muscle 29 and crossing over the lower ribs 31. This area undergoes a large degree of expansion and contraction during respiration and causes respective increases and decreases in the tension across the sensors, thus producing changes in resistance.
  • EKG Sensors
  • [0174]
    EKG sensors are pads 7 of conductive rubber wired to the electronic circuit of the LSDS contained on a PC board within the central housing 3 together with the battery. They are sewn to the back of the housing extensions so that they will be in direct contact with the wearer's skin. A small wire (not shown) is threaded into the rubber and connected to a longer wire (or other pathway) to create an electrical connection to the PC board. The wire is attached so that it will not come into contact with the wearer's skin.
  • [0175]
    Another, more mass producible embodiment of the LSDS is similar in form, but this alternative design includes the following features:
    • 1. The housing extensions are fabricated from an injection molded elastomer. The respiration sensors and EKG electrodes (also molded) are embedded into the housing extensions using a combination of multi-shot molding and mold-in-place techniques. Sealing lips are molded into the ends of the housing extensions that fit into the housing.
    • 2. The housing is injection molded and is assembled using either solvent bonding or ultrasonic welding rather than screws. This provides both increased strength and a water-tight seal. Contacts for the battery charger may be molded into the case.
    • 3. The rear half of the case may be molded from a flexible material for greater comfort. Alternatively, a pad, cover, or coating of a soft, textured material may be applied to the outer surface of the rear half of the case.
    • 4. The harness (not shown) may be formed from an elastic material that does not have the Velcro “loop” surface but does present a soft, textured surface against the skin. Velcro or other types of adjustable devices may be attached to the strap ends to make them adjustable and removable.
  • [0180]
    Due to resilience of the straps, the EKG electrodes are able to remain in contact with the same portion of skin as the subject breathes, and moves (as in walking, etc.), rather than having the electrodes slide over the skin. This significantly reduces the surface resistance where the skin and the electrode are in contact.
  • The Respiration Flex Sensor
  • [0181]
    The respiration sensor thus employs a novel deformation transducer element 19 that varies in electrical resistance as the chest or abdomen expands and contracts due to respiration. The respiration sensor provides relatively high signal levels that can easily be interfaced to a recording or transmitting component.
  • [0182]
    The novel transducer of the flex sensor is employed to produce an electrical resistance that varies with applied tensile, compressive, or bending loads. The basic structure consists of a flexible, variable resistance element 19 and a compliant backing or support element 21. The resistance of the flexible element increases as its radius of curvature decreases. It has a minimum resistance value when flattened. Two such elements are arranged on the extension so that each flexible element has a preset curvature when no load is applied. A tensile load while taking a breath will tend to reduce the curvature, thus decreasing the resistance; a compression load will act oppositely. Bending loads will similarly cause the resistance to increase or decrease depending on the direction of flexure. The backing or support element acts as a spring and limits the degree of deformation of the flexible element. This results in the change in resistance being approximately proportional to the applied load.
  • Respiration Tension Sensor
  • [0183]
    Various structures could be used to hold the transducer against the subject to detect respiration or other motion. The transducer means may be employed in one of several configurations. In one configuration it is employed as a tension sensor. The transducer is mounted to an elastic strap 11 such that the transducer is subjected to the full tensile load applied when the strap is stretched along its length. The strap, which is formed into a belt that fits around the chest or abdomen of the subject, is fabricated or adjusted to a length that insures that it will always be loaded in tension as the subject breaths or moves about. As the subject inhales and exhales the tension on the strap increases or decreases correspondingly. This creates a corresponding change in the electrical resistance of the transducer as described above.
  • Respiration Bending Sensor
  • [0184]
    In another configuration shown in FIG. 5, the transducer means is employed as a bending sensor that could be embodied in a patch 33. As shown in FIGS. 6-8, the transducer is attached between two projecting arms such that the rotation of either arm relative to the other will produce a change in electrical resistance. A flexible pad or backing 37 is applied to one side of this transducer assembly. A pressure applicator 39 is provided to compress the entire assembly against the subject's abdomen, oriented so that the flexible pad is placed flat against the skin. The pressure applicator may consist of a belt or strap, an external clamp or fixture, or an adhesive pad 33 that attaches to the surrounding skin. The pressure applicator is configured such that force is applied near the proximal and distal ends of each projecting arm with approximately equal force so that the flexible pad conforms to the curvature of the skin. The pressure applicator is further configured such that the mechanical compliance of the pressing elements is greater at the proximal ends than at the distal ends of the arms. When the subject inhales the abdominal wall expands. At the proximal ends of the arms the greater compliance acts to resist this motion to a lesser degree than at the distal ends. The result is a relative rotation of the two arms and a corresponding change in resistance of the transducer means. When the subject exhales the rotation is reversed, causing an opposite change in resistance.
  • [0185]
    Additional embodiments may be generated by employing multiple transducers and multiple straps, harnesses, or pressing devices. Further, the strap or pressing devices may be fabricated as, or incorporated into, a garment, and may support additional sensors or other devices. In either embodiment, the varying electrical resistance may be converted into a voltage or current signal using a variety of electrical circuits and may be converted to a digital or modulated format for additional processing.
  • [0186]
    FIGS. 6, 7, and 8 show schematically the alternate means of sensing respiration using a similar sensing element but without the need for a belt around the subject. In this configuration the sensing device is pressed against the abdomen of the subject. FIG. 6 shows the configuration at a neutral position. The skin and underlying tissue of the abdomen 35, shown in section view, are pressed against by two flat extensions 37 that are connected by an arched section 41 on which a resistive sensing element is mounted in the manner previously described. Similarly, a rotation upward or downward of one attached object relative to the other will cause a respective decrease or increase in flexure and thus a respective decrease or increase in resistance. A rigid or semi-rigid backing 39 is fixed at a short distance from the skin surface. Compliant elements 43, 45 fit between the backing and the flat extensions and act to press the flat extensions against the skin. The compliant elements may be springs, foam rubber, or any other springy material. The compliant elements 45 at the proximal ends of the extension have a different degree of compliance than the compliant elements 43 near distal ends, either using different material or different geometry. In this illustration the compliant elements at the distal ends may be considered to have the greater compliance or to be rigid. The abdominal wall can be considered as an elastic surface that will deform when pressed by the flat extensions. Not shown in the illustration is a pad or separator that would typically lie between the flat extension and the skin and which would act both to protect the electrical elements from moisture and to more smoothly distribute the force applied to the skin for better comfort.
  • [0187]
    FIG. 7 shows the effect on this configuration when the subject inhales. The abdominal wall expands, increasing the force against the flat extensions. Because the abdominal wall is elastic, the force will be distributed against the flat surface and balanced by deflection of the compliant elements. The more compliant elements 45 will deflect to a greater degree, causing a rotation of the flat extensions and increasing the flexure of the center section and thus the electrical resistance of the attached resistive element. FIG. 8 shows the opposite effect when the subject exhales and the abdominal wall contracts. The flat extensions rotate in the opposite direction, reducing the flexure of the center section and thus decreasing the electrical resistance.
  • [0188]
    The geometry and configuration of this type of sensing element can be varied in many ways. The required factors are the application of a force against the skin, a differential compliance such that a differential motion results from expansion and contraction of the abdomen, and a resistive sensing element placed so that its degree of flexure changes as a result of the differential motion.
  • [0189]
    FIG. 5 shows two preferred locations 33 for this type of respiration sensing configuration against the abdomen 47. The configuration may be placed to the side, directly below the ribcage or across the centerline of the body.
  • [0190]
    An electronic circuit is provided for analysis of signals affected by the flex sensors to determine respiration rate. The circuit simply looks for high and low peaks in the input signal and determines the peak to peak (p-p) time and amplitude. The results are compared to predefined min and max cycle times and a threshold amplitude to determine the presence or absence of breathing. The cycle period, p-p amplitude (arbitrary scale), and ratio of inhalation to exhalation times are reported. The analog input is digitally filtered to remove signals above ˜1 Hz. A second order filter would remove “movement” signals. A secondary circuit may be applied to “score” the output signal over a longer period, perhaps 60-180 seconds, and so produce a more reliable estimate of the presence of absence of breathing. This may take the form of “minimum of X seconds of breathing detected during the previous Y seconds.”
  • [0191]
    In addition to detecting respiration rates, the electronic circuit associated with the flex sensor reports the presence of body motion as seen as signals above 1 Hz. After applying a 2nd-order high pass filter to the analog signal, the result is rectified (absolute value). The result is compared to a reference. If it is greater than the reference an output flag is switched on and a timer is (re)initialized. The output remains on until the timer runs out—typically in 0.1-0.5 seconds. The intent is that activities such as walking will cause the output to remain on continuously. Alternatively, the rectified signal may be processed with a 1st order low-pass filter with a >1 sec cutoff to generate an envelope signal. The envelope signal is compared to a pre-defined reference level and a yes/no output is generated. This could be expanded to report multiple levels, signal frequency, or peak values.
  • Motion Sensing
  • [0192]
    An accelerometer is included in the electronic circuit as the primary means for motion sensing and sensing the orientation of the subject (e.g. standing, lying down). However the flex sensor may be used by the electronic circuit to provide a backup signal for the accelerometer. A useful feature of the motion signal obtained from the respiration detector is that it is shows particular sensitivity to localized upper-body motions. This contrasts with the accelerometer, which is sensitive to any acceleration of the torso.
  • [0193]
    The electronic circuit may also include correlation of multiple sensor inputs, particularly of the respiration sensor and accelerometer. Alternately this can be provided in software. The primary intention is to provide improved confidence levels for the quality of processed signals. A simple example is that the apparent presence (or absence) of a detected respiration signal may be considered meaningless if a large accelerometer output in a similar frequency range is detected.
  • Sensor Electronics
  • [0194]
    The LSDS gathers certain physiological information and sends it first wirelessly to a local receiver or transceiver for retransmission to a separate computer station such as a PC or PDA. The unit measures heart rate by detecting and timing ECG R-waves, determines physical activity and orientation using an accelerometer, determines respiration rate by reading a chest expansion sensor, and measures temperature. These life signs are then analyzed using a health state determination algorithm. The resulting health indications, plus the raw data behind them, are transmitted out of the LSDS preferably every two seconds, or this period could be allowed to vary. Alternatively, if nothing has changed, energy could be saved by transmitting a “nothing changed” signal.
  • Major Component Overview
  • [0195]
    The sensor contains an 8-bit processor surrounded by various sensor inputs and an RF transmitter. A block diagram of the electronics is shown in FIG. 13. The full circuit diagrams are presented in FIG. 14 (Processor and Power Control), FIG. 15 (ECG Front End), FIG. 16 (Accelerometer and RF circuit), and FIG. 17 (Respiration Circuitry).
  • [0196]
    Referring to FIG. 13, a microprocessor 49 such as an Atmel AVR Mega32 processor is used. Typical requirements for the processor are low power draw, suitable program memory (16K words), suitable RAM memory (2K bytes), EEPROM for non-volatile storage, general purpose I/O, analog inputs, external interrupts, versatile timers, high and low speed clocks (4 MHz and 32.768 kHz), flexible low-power sleep modes, in-circuit programmability, and easy to use development tools.
  • [0197]
    To conserve battery life, the processor makes extensive use of sleep modes. There are two crystals attached to the processor, one that runs at 4 MHz, and one running at 32.768 kHz. The high speed crystal runs the processor when it is awake, and the lower-frequency crystal keeps the internal timers running when both a wake and in the low-power standby mode.
  • [0198]
    A Lithium-Polymer (LiPoly) battery is used because of its high power density, various thin packaging options, and lack of memory effect (as is experienced with NiCad and NiMHd battery chemistries). The preferred battery used in the LSDS strap is rated at 560 mAHr.
  • [0199]
    The battery voltage is monitored by feeding ½ of the battery voltage into one of the processor's A/D inputs. This ½ Vbat is also used by the ECG detection circuitry and is a convenient voltage for monitoring battery health. A fully charged battery is at about 4.2V, and the battery will operate normally all the way down to 3.2V, at which point the circuit will be shut down to avoid erroneous reports. Beyond 3.2V the voltage will drop fairly rapidly when under load.
  • [0200]
    The output of the sensor is intended to be transmitted to a local receiver for further transmission to a more remote station. The RF Monolithic (RFM) TX6000 is a 916.5 MHz transmitter 53 that operates at 3V and draws <10 mA when on and draws virtually no current when in sleep mode (between transmissions). A 1 kHz Manchester-encoded data stream is sent out the RF transmitter once every two seconds. The transmitter uses simple on-off keying, thus only drawing power when transmitting a “1”.
  • [0201]
    Transmit range depends on the length and shape of the antenna, the orientation of the antenna, and how close the antenna is to the body and the electronics in the LSDS strap. Maximum range is about 50 feet. Lesser range would consume less power, reduce interference with other devices and reduce detection by an adversary.
  • [0202]
    A pair of conductive rubber pads 55 picks up the ECG signal generated by the heart. A single-ended input circuit (one input is ground) amplifies and filters the ECG input. An adaptive comparator looks for the high slew rate of the R-wave component of an ECG pulse, allowing the circuitry to detect strong and fast heart rates as easily as weak and slow ones. The analog “front end” is a slew rate detector circuit with sensitivity down to 0.15 mV when no appreciable noise is present. This analog circuitry draws very little current, allowing it to remain continuously powered-up when the LSDS is on-body.
  • [0203]
    An ADXL202E two-axis accelerometer 57 is used to detect both activity level and orientation. This version of the device puts out a pulse-width-modulated pulse train that is timed by the processor. It is turned on by the firmware only when read, and left off at all other times. Only one axis, the one that corresponds to the vertical axis when the wearer is standing upright, is used. Thus the sensor can distinguish a standing subject from one lying down, but cannot tell on which side he/she is lying. The second accelerometer axis can easily be used, allowing side-to-side orientation sensing as well.
  • Respiration Sensor and Circuitry
  • [0204]
    Tension sensors 59 are built into each end of the LSDS plastic shell. These variable resistors change value as the chest expands and contracts. The LSDS circuitry changes this resistance into a voltage that is then frequency limited using a 0.25 to 2.5 Hz band pass filter. The resulting signal is then sampled by the processor using one of its built-in analog-to-digital inputs, while the rest of respiration sensing is handled in firmware.
  • [0205]
    The sensor has a nominal resistance of approximately 5K ohms. The resistance change at maximum load: approx. (−500) ohms. The required analog bandwidth is 0.06 Hz-4 Hz. The low pass (4 Hz) cut-off matches the available 8 Hz sampling rate. This should be adequate, although 6-8 Hz appears to be optimum. A 1st order filter may be adequate for the 4 Hz cut-off. The sensitivity of the sensor falls off at higher frequencies. Aliasing of signals up to 10 Hz will be correctly interpreted as body movement.
  • [0206]
    The high pass (0.06 Hz) cut-off has been chosen to match the slowest normal breathing rate. It is intended primarily to provide decoupling of the sensor's DC offset.
  • [0207]
    When the sensor is to be used to sense heartbeat, an analog bandwidth of approx. 75-100 Hz is required. Because this would most likely be used as an occasional “last resort” measurement, it may not be preferred to provide analog wave-shape detection. The microprocessor could simple sample at a 150-200 Hz rate for a period of 5 to 10 seconds and process the signal to determine whether a heartbeat is present.
  • [0208]
    A Maxim MAX6613 temperature sensor 61 is use to measure the temperature of the circuit board. Since the plastic LSDS enclosure is pressed snugly against the skin, the temperature read by the sensor tracks the true skin temperature after a short thermal delay period. The sensor has better than one degree C. accuracy over a 5 C to 50 C range. The sensor converts temperature into a voltage in a very linear fashion, and this analog result is fed into one of the processor's A/D inputs. Since it draws so little power, it is left on when the strap is on-body.
  • Power Control
  • [0209]
    In order to extend battery life to its fullest potential, the ability to turn sections of the circuitry on and off is crucial. Power switching is under control of the processor. Some devices have power control inputs (e.g. the RF transmitter), while other devices are turned on and off using a high-side low resistance FET switch. Power to these devices is gated by the FET transistors whose gates are attached to processor outputs.
  • Power Management
  • [0210]
    A simple representation of this power management scheme is shown in FIG. 1S. Some switches will be closed whenever the LSDS is on-body, and other switches are closed only when needed. The leads-on detection circuitry 63 is always attached to power since the processor always needs to know when the LSDS strap has been put on-body or taken off-body. Similarly, the processor is also always powered up, although it enters a low-power mode whenever possible.
  • Program Organization
  • [0211]
    The firmware program stored in the microprocessor is organized according to the major tasks that are to be performed. A task manager schedules the execution of each of the tasks. By having each task operate as a state machine, task switching can be done at a very fast rate, resulting in the illusion that all tasks are running simultaneously. FIG. 19 is a block diagram of the major tasks.
  • [0212]
    Each task has a different operating mode, depending on whether the strap is on- or off-body. In most cases nothing is done when the strap is off-body. When on-body is detected (and debounced) by the sleep manager tasks, all of the other tasks turn on certain circuitry (as needed), initialize certain variables, and begin to perform their respective functions.
  • R-Wave Monitoring
  • [0213]
    The heart rate algorithm receives an interrupt every time an ECG pulse is detected. Since EMG and electrical noise caused by skin stretching and ECG sensor contact motion all cause interrupts on the ECG input to the processor, the heart rate algorithm performs a good deal of filtering in order to isolate the desired R-wave pulses.
  • Orientation Monitoring
  • [0214]
    Orientation is determined by looking at the value of the accelerometer. Since the accelerometer is calibrated to detect gravity, a +1G acceleration means the unit is upright, 0G indicates horizontal orientation, and somewhere in-between means the strap is at an angle. Orientation is only measured when the activity level is between low and none.
  • Activity Monitoring
  • [0215]
    Activity is measured periodically in order to determine how much movement the user is experiencing. The accelerometer is turned on and sampled at a 4 Hz rate in order to reduce battery consumption. A sudden/short movement may be missed, but the next movement may be measured instead. This task simply looks for the highest amount of acceleration that is sampled, and holds this level for a few seconds, as a peak-hold circuit would operate.
  • Temperature Monitoring
  • [0216]
    Since temperature is not expected to be changing at a fast rate, temperature is only measured every 15 seconds in order to save battery power. This task has multiple states since the process includes reading an A/D input channel and then converting the result into a temperature.
  • [0217]
    Power control settings may be variable, e.g. so that at low battery the system might choose to collect and transmit fewer vital signs, provided that sufficient remain for the life assessment.
  • Respiration Monitoring
  • [0218]
    The respiration monitor task samples the bandwidth limited chest expansion voltage at an 8 Hz rate, and then performs a simple analysis to determine when breathing is occurring. The algorithm first determines when the wearer is inhaling or exhaling. This is done by looking at the relative change in the sampled signal, effectively taking a first order derivative that removes the DC component of the signal. Once a binary signal (inhaling or exhaling) is produced, it is timed and analyzed for consistent behavior. If several similar (+/−25%) timed breaths are seen, they are averaged together and used as the final respiration value. If no consistent breaths are seen in a 30 second period, the respiration rate is set to “unstable”. If no chest expansion/contraction is seen for over a minute, respiration rate is set to zero.
  • Health State Manager
  • [0219]
    This task runs an algorithm (or algorithms) that determine the current health of the wearer based on all available physiological information. Recent historical physiological information is kept in an array and is used to determine both the health of the user and the confidence of the assessment of health. The resulting health state is not used on-board, but is instead simply transmitted as part of the RF packet. Alternatively, the assessment could be used locally to decide the rate at which to monitor events.
  • RF Manager
  • [0220]
    This task transmits data. Transmissions are preferably repeated every two seconds or at a variable rate. Whenever it is time to transmit a packet of data, the RF manager task simply gathers the most recent physiological information, calculates the appropriate checksum (or other error correcting codes), and builds a packet of information for transmission. In order to maintain tight timing on the Manchester encoded data, a timer interrupt is used to shift out the actual data bits. In other words, once the packet has been built by the RF manager task, the timer interrupt takes over and shifts out all of the data with the appropriate timing.
  • Battery Monitoring
  • [0221]
    The battery monitor task periodically measures the battery voltage level in order to determine the health of the battery. Since batteries tend to have a “knee” at which the voltage drops off rapidly, only a “low” and “not yet low” determination can reasonably be made. Any voltage above 3.6V is interpreted as a healthy battery. About 95% of the time the battery will be above this “knee” voltage”. When the voltage drops below 3.6V, the battery monitor interprets this as a “low” indication. When the battery drops below 3.2V, the battery monitor changes its indication to “dead”, meaning that there is not much time left before the strap stops operating. The exact timing for each of these battery levels depends on strap use, how well the battery was charged, and how old the battery is. In general, a fully charged battery will operate over two weeks on-body before entering the “low” state, then operate another hour or more before entering the “dead” state. Even then, the unit should continue to operate with a “dead” battery for 10 minutes or more.
  • Sleep Manager
  • [0222]
    This task reduces battery consumption by putting the unit into a power-saving sleep mode as often as possible. The firmware puts the processor to sleep even when the strap is on-body. The difference in sleep mode use between on-body and off-body operating modes is that when on-body, the unit wakes up more often (8 times a second). The sleep manager looks at all of the states of all of the tasks when determining if the unit can go to sleep. If all of the tasks are in their respective “idle” states, and no action-pending flags are set, the firmware instructs the processor to shut down the main 4 MHz clock and wait for a timer or interrupt event to wake it up again. The unit spends almost all of its time in a low-current sleep mode, even when on-body.
  • Miscellaneous Functionality
  • [0223]
    There are three timers running in the background, two being at high speed and one being at a slower 8 Hz rate. None of the tasks described above run any more often than 8 times a second, allowing the processor to spend most of its time in sleep mode, when on-body. One high speed timer is free running and is used to measure short time intervals. The other timer is started and stopped as needed to provide additional timing resources.
  • Improved EMI Rejection.
  • [0224]
    The Flex Sensor may act as antennae to pick up unwanted electromagnetic noise. While the output signal may typically be filtered to remove this noise, it is usually preferable to minimize the initial noise pick-up. The standard Flex Sensor has one resistive and one conductive strip, joined at the end of the sensor opposite the contacts. Improved EMI rejection will result from a configuration with one resistive strip and two conductive strips. The conductive strips are placed on both sides of the resistive strip, and all three strips are joined at the end opposite the contacts—now three contacts instead of two. In connecting to a circuit, the two contacts to the conductive strips are connected to a fixed voltage level, typically either ground or supply voltage, while the contact to the resistive strip is used as the output. By these means the output portion of the sensor is completely surrounded by a portion that acts as an EMI shield.
  • Improved Moisture Resistance.
  • [0225]
    The mode of operation of the Flex Sensor, in which micro-cracks open on the surface of the resistive coating, makes it inherently susceptible to moisture. Water and other liquids can flow into the micro-cracks, effective shorting these gaps in the conductor. In air, suspended water molecules and other suspended ionic particles may similarly enter the micro-cracks with similar results. A cover sheet with an adhesive backing may be used to protect the resistive element.
  • Reversed Bending
  • [0226]
    Two Flex Sensor elements may be printed back-to-back on a single substrate. If the substrate is bent in either direction, one of the elements will increase in resistance. The two sensors may be monitored independently. Alternately, the two sensor may be wired in series and connected between two fixed voltages, thus creating a voltage divider. The voltage output of the divider, measured at the junction between the two elements will increase when the device is bent in one direction and decrease when it is bent in the other direction.
  • [0227]
    The fabrication process may be modified to change the operating range. Immediately after the resistive ink is applied the substrate is bent into a concave shape; the ink is on the inside surface and so its length contracts relative to the substrate. The ink is allowed to dry, the substrate is straightened, and the material is processed to produce micro-cracks. Because of the contraction of the ink the cracks will be partially opened when the sensor is straight. The operating range of the sensor is shifted to provide a useful output signal with bending in both directions.
  • Transmission
  • [0228]
    Although to reduce bandwidth, processing is preferably accomplished on the LSDS, some processing may be left for the host (receiver). The LSDS processor transmits amplitude and duration values for respiration cycles but does not apply any threshold tests. The host (receiver) has the task of determining whether the amplitude and duration values fall within acceptable limits.
  • [0229]
    A running average of the amplitude and duration values of the last four respiration cycles is transmitted to the host processor, rather than the values for the current cycle alone. This provides a more consistent output, but may introduce a degree on indeterminacy.
  • [0230]
    A small hysteresis value is applied to the respiration signal to minimize false “end of cycle” readings due to noise in the signal. The hysteresis value is dynamically adjusted based on the amplitude of the previous cycle.
  • Communications Protocol Requirements
  • [0231]
    The communications protocols in use by this system must provide error detection or correction codes to ensure that the data is received as sent. The protocols used must provide the capability to be assigned to an upstream unit, so that a set of sensors may be assigned to a single hub, and a set of hubs may be assigned to a single remote station.
  • [0232]
    A local protocol provides the transport of data between one or more sensors and a single hub. Since there may be many sensors, the local data packet format is extensible, not requiring changes to the hub to accommodate new sensor additions. Gaps in the sensor data must be accounted for, either by providing a filler packet (of perhaps just a timestamp), or by the indication that the sensor is no longer communicating.
  • [0233]
    A distant protocol provides the transport of data between a hub, and the remote station. This protocol must allow for interruptions in the data stream, with later recovery of data stored within the hub.
  • User Interface Requirements
  • [0234]
    The hub subsystem may provide a limited user interface in order to provide local health display (e.g. red/yellow/green LED's), and possibly a local selection mechanism to facilitate the initial association of one or more sensors to a specific hub. A more elaborate user interface is also possible if energy constraints are satisfied. The association of a specific hub to a remote station may be performed at the hub, or via the remote communications link, either to a medic PDA, or back to a remote station.
  • [0235]
    The remote subsystem has a more complex user interface to allow for the display of the basic status of multiple hubs within a single display, as well as being able to display additional status and data details from at least a single hub.
  • Medic PDA
  • [0236]
    The medic PDA subsystem has a user interface capable of displaying a list of hubs to connect to, and a mechanism to connect and display the detailed data as delivered by the hub.
  • Processing Requirements
  • [0237]
    The sensor subsystem is designed to: Capture and convert the analog data into digital form, perform error detection processing, to validate the proper application and operation of the hardware systems, battery status, etc., perform combined analysis of the biometric data, yielding the overall health metric, assemble and transmit the periodic data packets to the hub subsystem, and accept data received from the hub subsystem, applying configuration or command sets to update operational parameters.
  • [0238]
    The hub subsystem is designed to: Collect the periodic data from the sensor subsystem(s), buffering samples for transmission to the remote station; Provide minimal user interface capabilities to display the overall health status, and allow for sensor subsystem selection to be performed; Perform additional health status processing if multiple sensors are available to a single hub; Provide the uplink processing and data packaging for remote/PDA accesses
  • [0239]
    The remote subsystem is designed to: Provide minimal status display of up to 20 hubs; Provide expanded status display of one selected hub; and Provide long-term data logging for all hubs connected.
  • [0240]
    The medic PDA subsystem is designed to: Establish a communications link to a single hub unit; and Provide display of all available sensor data and status information.
  • Communication Protocols
  • [0241]
    There are two communications protocols required as part of the complete LSDS design. The first protocol transfers data from the vital signs sensor to the hub, which in turn acts as a concentrator and relay to a remote station.
  • [0242]
    The Sensor-Hub protocol provides the communications locally between one or more body-worn sensors, and a physically proximate hub/gateway.
  • Packet Formats General Packet Structure
  • [0243]
    In a preferred embodiment, every packet is required to provide the indication of the start of packet, which is done by encoding the packet length, followed by the ones complement of the packet length as the first two bytes. The packet length is defined as the number of bytes (octets) of the data payload, plus two so that the 16-bit CRC is included in the length. The packet data payload follows the header, and is able to be up to 253 bytes in length. The validation CRC follows the payload data, and is a standard CCITT polynomial CRC.
  • [0000]
    Byte # Description
    0 Packet length (n)
    1 Ones complement of packet length (~n)
    2 through n Packet data payload
    N + 1 MS Byte of 16-bit CRC
    N + 2 LS Byte of 16-bit CRC
  • Sensor to Hub Payload Format
  • [0244]
    There are two kinds of data transmitted from the sensor to the hub: sensor data and control data. Sensor data contains the data values obtained from one or more vital signs sensors that are present. Control data is sent in response to a command from the hub.
  • Sensor Data Packet Format
  • [0245]
    The format of a Sensor Data packet contains, at the minimum, the Sensor ID field, the first Data Present byte, and the health status field. If indicated in the data present field(s), other data will be present in the packet, in the order defined in the data present field.
  • [0000]
    Sensor ID 8 bits Assigned ID of Sensor
    The Data Present is one or more bytes, with a bit set for each position that is encoded in the packet.
  • First Data Present Byte
  • [0246]
  • [0000]
    Bit
    position Description
    0 Health Status
    1 Heart Rate
    2 Breath Rate
    3 Motion
    4 Vocalization
    5 Temperature
    6 Battery Status
    7 Clear indicates end of data field bytes
  • Second Data Present Byte
  • [0247]
  • [0000]
    Bit
    position Description
    0 Unused
    1 Unused
    2 Unused
    3 Unused
    4 Unused
    5 Unused
    6 Unused
    7 Clear indicates end of data field bytes
  • [0248]
    The health status field is the output of the overall health algorithm. This output will take the form of a three-state variable, followed by an integer confidence rate. The heart rate field contains either the heart rate numeric value, in the range of 20-250 beats per minute, or an indication of a hardware or software problem status. The breath rate field contains either the breath rate numeric value, in the range of 1-100 breaths per minute, or an indication of a hardware or software problem status. The motion field contains the indication of activity, as measured by an accelerometer, and will be in a 4-state range where lower value indicates less activity. A vocalization field contains data from the sensor. The temperature field contains the current body temperature in degrees Celsius. The battery status field contains a three-state (high, medium, low) value indicating the charge left in the battery. The sensor control packet is sent in response to a command from the hub. Its contents are dependent on the command that it is responding to. The Sensor ID contains the 32-bit unique ID for a sensor. This is used as part of the process of associating a sensor to a hub.
  • [0249]
    The data transmitted from the hub to the sensor contains command data only. These messages are for providing configuration values, and retrieving status information that is not periodically transmitted.
  • Sensor Commands
  • [0250]
    Attach Sensor: This command causes the sensor to become associated with the sending hub, and assigns an 8-bit sensor id to the sensor.
  • [0251]
    16-bit CRC generation and validation: CRC generation is preferable to a simple checksum due to the larger number of errors that a CRC will catch, that a checksum will not.
  • Error Handling
  • [0252]
    At a minimum, the CRC on each data packet will indicate the success or failure of data transmission. Any packet that fails the CRC check will be discarded, and will not used in determining either the state of the system, or the health of the person it is attached to. If the underlying transport protocol does not support error correction measures such as retransmission, then a data packet that fails its CRC check will be discarded, and an indicator of this data loss inserted into the data stream.
  • Hub-Remote Protocol
  • [0253]
    The hub-remote protocol provides the information transfer between the hub, and a remote viewing station that may be either a medic PDA, or a grouped display.
  • Remote Display of Data
  • [0254]
    The software is divided into an upper and lower end, based at the point in which a valid packet has been received. In the case of a live connection, this is checked for in the timer loop once every 100 msecs, polling for new data received by the serial interface and collecting it into a packet. In the case of a replay file, a two-second timer is used to read in the next packet ‘received’.
  • [0255]
    A valid packet, whether from a file or from the serial interface, is passed to the main message loop of the application. When this is received, the packet is parsed, updating the corresponding displays with the newly received data. In addition, the data received is formatted into an ASCII string in hexadecimal format, and displayed in the LSDS communications field. Live collections additionally count the number of packets that contained header errors or checksum errors and update their respective fields.
  • [0256]
    If the health status algorithm is enabled, it will be sent copies of the newly received data, which are placed into individual parameter data buffers for the next analysis phase. Preferably, once per second, the health status algorithm is executed on the data buffers, updating the display of the health status, along with the confidence score of that determination.
  • Configuration Dialog
  • [0257]
    The configuration dialog contains the controls to select between data input from a live sensor, and replay data from a log file, serial port setting controls for both the LSDS sensor, and the optional Propaq interface, and enable checkboxes for running a session with a Propaq collecting data, as well as enabling or disabling the local health status algorithm for processing on received data. It is preferred that when using the local health status algorithm, that a single ID be filtered for, as conflicting data from multiple sensors will invalidate the operation of the health status algorithm.
  • Main Dialog
  • [0258]
    The main dialog is where all of the relevant information from data collection and processing are displayed. The dialog is broken up into groups of related data:
  • [0259]
    LSDS Communications displays provides a view of the communications from the LSDS sensor. As valid packets are received, the payload portion is displayed in ASCII hexadecimal notation within a scrolling text box. If header or checksum errors are detected, then the corresponding error counts are incremented.
  • [0260]
    Health Status displays contains the processed data from the LSDS sensor. It also contains the display of the Health State and confidence score both from the LSDS sensor, as well as the local implementation of the algorithm.
  • [0261]
    The set of icons and the states they represent are as follows:
  • [0262]
    BLACK health state: this means that no data has been received for 16 seconds
  • [0263]
    BLUE health state: this means that a valid determination is unable to be processed from the current data.
  • [0264]
    RED health state: this means that the health state is in critical condition, or possibly dead.
  • [0265]
    Yellow health state: this means that the health state is abnormal.
  • [0266]
    Green health state: this means that the health state is normal.
  • [0267]
    Red exception health state: this means that one of the red exception states has occurred.
  • [0268]
    The Propaq comms display provides a single status line indicating the operational mode of the Propaq communications interface, display of the received HR and RR values from the Propaq, and the difference, if any, between those values and the values determined by the LSDS sensor.
  • [0269]
    Every time the application is run, the data delivered as valid packets is copied out to the text replay file. This occurs after the id filtering is applied, and will therefore correspond to the data trace of a single LSDS sensor unit if filtering is active. The format of the data is in human-readable ASCII hexadecimal notation, one line per packet. The format of the packet is documented in the RF protocol document.
  • LSDS Packet Reception and Validation
  • [0270]
    The incoming data is received and buffered by the system serial device driver. Once every 100 milliseconds, any incoming data is collected and scanned for the expected start of packet sequence as documented in the RF protocol document. Extraneous data bytes are discarded after being logged in the binary packet file. Once a valid start of packet sequence has been detected, a counter is incremented for each new data byte, until the expected number of bytes have been received. Once a complete packet has been collected, then the checksum algorithm sums the data values, and compares it to the expected checksum field. If it is equal, then the packet is sent on for processing as a valid packet, otherwise, the data is ignored, and a new start sequence is searched for.
  • LSDS Data Field Processing
  • [0271]
    Each packet that has been validated contains essentially a snapshot of the LSDS sensor state. This data is validated against the expected range of values before being displayed, and if it is out of range, a display of ERR is used to indicate this. Additionally, if the alternate health status algorithm is active, then the data is sent to it to be used for evaluating the next health status result.
  • [0272]
    The ID data field is used only if sensor ID filtering is active. If the ID matches the filter ID, then the rest of the packet is processed, otherwise it is simply discarded. The Heartrate data field is used to display the current heartrate in the main dialog, as well as being subtracted from the most recent Propaq HR value to generate the delta HR field, if the Propaq interface is actively collecting data from a Propaq monitor. The Respiration data field is used to display the current respiration rate in the main dialog, as well as being subtracted from the most recent Propaq RR value, to generate the delta RR field, if the Propaq interface is actively collecting data from a Propaq monitor. The temperature data field is used to display the current skin temperature in the main dialog. The acceleration data field is used to select the appropriate label in the acceleration display in the main dialog. The orientation data field is used to select the appropriate label in the Orientation display in the main dialog. The Health status data field is the Health State as determined by the sensors' internal health state algorithm. It is used to determine the display in the Sensor Health State display on the main dialog. Confidence score data is the confidence score calculated by the sensors' internal health state algorithm. It is used to update the display in the Sensor Confidence field in the main dialog.
  • Health Algorithm Implementation
  • [0273]
    The design of the health status algorithm contains five processing steps: Data Gathering and buffering; Data summation (e.g. averaging) and conversion from numeric/symbolic into qualified range data; Rule lookup processing, Confidence scoring; and Result display. Of these steps, the first one is done asynchronously because of the nature of the communications medium, and is driven by the reception of data packets from the LSDS sensor. A one-second timer drives the rest of the processing steps, with all steps running to completion and generating a new health status and confidence score.
  • Data Gathering and Buffering
  • [0274]
    Each parameter has a 16-deep FIFO ring buffer for the collection of data from the sensor. Each sample in this buffer has, in addition to the value field, two flags, one to indicate that data was received, and one to indicate whether or not the data is valid. The current sample index of these buffers is incremented once per second, whether or not data is received. As the current write index is incremented, the new sample index flags are cleared to indicate that no data is present. As LSDS sensor data is received, it is copied into the current sample index in the ring buffer. A minimal amount of processing is performed, only to determine if the data value is within the defined valid range of the sensor.
  • [0000]
    Conversion from Source Data to Qualified Data
  • [0275]
    Each parameter ring buffer is processed to provide the average value of the data within the ring buffer. For numeric parameters (HR, RR and Temperature), this is simply the arithmetic average (sum of the valid data values divided by the number of valid samples). For symbolic parameters (Acceleration and Orientation), this is done by counting the number of each enumerated value, and returning the one that has the greatest count. In the case of equal results, the enumeration with the lowest value is returned. This average value is then compared to the defined range boundaries, and the qualified data range value is returned.
  • Rules and Rule Processing
  • [0276]
    A rule contains a bitmap of qualified data range results for each parameter, along with a result state to be used when a match is found. Each parameter field in a rule will contain at least one of the defined qualified data results, and may contain the composite result masks. Once the current states of the ring buffers has been obtained, these states are compared to each rule until either a match is detected, in which case the corresponding health state is used, or all rules have been checked, in which case the default state of BLUE (unable to proceed) is used.
  • Confidence Scoring
  • [0277]
    The confidence scoring is performed last, since one of the input parameters is the determination of whether or not the current Health State has changed.
  • [0278]
    Once the health state and confidence score have been determined, then the new values are displayed on the main dialog, in the Hub Health State and Hub Confidence fields.
  • Heart Rate Calculation Algorithm
  • [0279]
    The algorithm works by tracking trends. A trend is defined as a somewhat consistent series of ECG, pulses based on their timing. Several times a second a decision is made to keep using an existing trend or to shift to using a new trend. This means that several processes must be running in parallel, one that tries to track an already established trend, one that continuously looks for a new trend, and one that determines which of these two has better data. Incoming ECG information is filtered before presenting it to the trend tracking routines in order to avoid spending time working on noise pulses. ECG data is averaged and filtered, and then converted into an actual beats-per-second value.
  • [0280]
    A block diagram of this algorithm is shown in FIG. 20. Since each incoming ECG pulse is time stamped, those remaining after filtering and noise cancellation can be processed in non-real-time. This is useful since past or future pulse information is sometimes required to get a better understanding of the trend being followed and to allow for more tolerance of missing or extra pulses.
  • Timing
  • [0281]
    The slower the timebase, the easier it is to run the algorithm on a simple, 8-bit processor. Additionally, a timebase that uses a lower resolution clock allows the timebase to run while the processor is sleeping, reducing the drain on battery power. However, a more course timer resolution increases error (reduces accuracy) and makes it more difficult to implement simple per-beat timing comparisons.
  • [0282]
    A reasonable compromise is to use a 32 Hz clock as the basic timer. This allows per-beat intervals to be timed accurately enough to determine if a trend is present, shifted, or lost. Although a 32 Hz clock is not nearly fast enough to accurately time heart rate on a per-beat basis, the averaging/filtering scheme described below looks at 4 seconds or more of ECG data. With a window of 4 seconds, a 32 Hz clock allows for better than 0.8% accuracy. A 32 Hz timer allows for an 8-second duration when stored as a simple 8-bit entity. This is plenty long enough for all pulse timing and averaging activities.
  • Process Timing
  • [0283]
    In order to keep processing power to a reasonable level and to allow the use of a small, inexpensive, and battery conscious microprocessor, processes are set up to run only at certain specific intervals, and this process repeat pace is kept to a fairly slow rate of once every eight seconds.
  • [0284]
    FIG. 21 shows how often each process is run. ECG pulse detection is performed whenever an ECG (or EMG or other unwanted signal) is seen, so its timing is sporadic and asynchronous to the rest of the process timing. The heart of the algorithm, which includes filtering trend tracking and analysis, is executed at an 8 Hz rate. Averaging/filtering is run only once every two seconds, and the resulting ECG rate is converted to a beats-per-second value every two seconds as well.
  • Algorithm Specifics
  • [0285]
    ECG, EMG and any other electrical impulse of sufficient magnitude cause an interrupt to the processor. The ECG pulse detection routine simply timestamps every interrupt and saves a record of its having happened. This information is used by the filter process. A flowchart of the ECG pulse detection interrupt is shown in FIG. 22. Since an incoming noise stream should not be allowed to flood the filtering process, the ECG pulse detection routine stops recording interrupts if too many ECG pulses are still waiting processing by the filtering process. The list of pending interrupts is cleared by the filtering routine on a periodic basis.
  • Low Pass Filter and Noise Cancellation
  • [0286]
    This process removes presumably incorrect ECG information by applying low pass filtering and noise cancellation. A low pass filter does not allow ECG pulses to come in too close in time to previous pulses, whereas noise cancellation simply deletes what appear to be extra pulses.
  • [0287]
    The low pass filter cutoff frequency is set to 8 Hz, which corresponds to a two-times sampling rate of 240 BPM. The filter works by throwing out incoming pulses that occur too close to the previous pulse. Since the filtering routine is run at an 8 Hz rate, the routine allows only one ECG pulse per ⅛ second period. If more that one ECG pulse is pending processing, only one is taken and the rest are ignored. A good example of when this filter is necessary is when there are echo ECG pulses due to both R and P wave detection. A flowchart of this filter is shown in FIG. 23.
  • [0288]
    Note that since this algorithm looks ⅛ second backward in time, a ⅛ second delay is introduced by this scheme. Once the ECG pulses have been processed, pulses seen in the previous ⅛ second window are passed on to the trend discovery and trend tracking stages of the algorithm.
  • [0289]
    As an example of how this filtering scheme works, refer to FIG. 25. The first labeled pulse is ignored since it occurs too close to the proceeding pulse. The second labeled pulse is ignored since it is an additional pulse within the same ⅛ second time window.
  • Look for a New Trend
  • [0290]
    New trends are recognized by looking at only the most recent ECG pulse timing. A trend is defined as somewhat consistent timing of ECG pulses. Since noise can be expected and ECG pulses may occasionally be missed, the trend acquisition algorithm needs to be tolerant of extra and missing pulses. This is accomplished by looking at intra-beat timing and deciding which timing appears most often. As long as extra or missing pulses do not appear more often than true ECG pulses, this process should be able to find the correct heart rate.
  • [0291]
    The algorithm works by looking at four most recent inter-beat intervals and developing a scoring based on the consistency of these intervals. Since inter-beat intervals are not going to be exactly the same, a +/−12.5% window is allowed. With this size window, a missing beat will clearly be detected, and although an extra beat may appear inside this window, the following correct beat will appear later in time much less than the window size.
  • [0292]
    By looking at only the last four inter-beat intervals, the effect is that of a sliding window, leaving older information behind quite soon. This allows the algorithm to lock onto new trends fairly quickly, and also allows it to track slightly changing heart rates. Each new ECG pulse or perceived missing ECG pulse causes an update in the “score” for how well a trend is being seen. The rules for scoring are as follows:
  • [0293]
    if 3 in a row have similar timing, score=high
  • [0294]
    if 3 of last 4 have similar timing, score=med. high
  • [0295]
    if 2 of last 3 have similar timing, score=medium
  • [0296]
    if 2 of last 4 have similar timing, score=low
  • [0297]
    Using FIG. 25 as an example, inter-beat intervals are tracked as they occur, left to right. Interval t1 is the normal heart rate, and it appears the most. When the extra pulse occurs, it creates the two shorter inter-beat intervals t2 and t3. Then, when a later pulse is missed, the longer inter-beat interval t4 is seen.
  • [0298]
    In order to keep extra or missing pulses from adversely affecting the heart rate being calculated by the averaging process, only the consistent inter-beat intervals are saved in the history array. Again referring to FIG. 25, since t1 is seen the most, only it will be saved in the history array.
  • [0299]
    A flowchart of this trend-acquiring process is presented in FIG. 26.
  • Tracking an Existing Trend
  • [0300]
    An existing trend is tracked by assuming the heart rate to be at a certain frequency, and then looking for more heartbeats at these expected intervals. Extra pulses are ignored in order to keep locked onto an existing trend in the presence of noise (extra pulses). Likewise, missing pulses are accommodated by assuming pulses to come at a certain time, and to allow for missing pulses as long as they sooner or later start showing up at the expected time. These actions are illustrated in FIG. 27.
  • [0301]
    In order to keep locked onto a slowly changing heart rate, a 12.5% window (+/−6.25%) of tolerance is allowed on each expected pulse. This size is selected since it is easy to calculate in integer math. With a tolerance window this wide, the heart rate can change at a reasonable rate while still allowing this process to remain locked onto the moving trend.
  • [0302]
    Since extra pulses are ignored and missing pulses are assumed present, a near or perfect harmonic shift in heart rate will not be noticed by this process. For example, a jump from 60 to 120 BPM will not be noticed since at 120 BPM, a pulse is seen at the same timing as when the heart rate was 60 BPM, and the extra pulse in the middle is simply ignored. This indifference to harmonic shift is acceptable since a “look for a new trend” process will identify the proper heart rate of 120 BPM, and its score will be higher than that generated by this process.
  • [0303]
    Also, there needs to be a mechanism by which this “existing trend” process is locked onto a new trend when that new trend is seen to be strong and stable. The mechanism works by unlocking the existing trend when its score is low, and then locking onto a new trend when the new trend is seen to exist. This is how a harmonic shift is ultimately resolved, forcing the existing trend to lock onto the new, correct trend.
  • [0304]
    A score is maintained for how well the trend is being tracked. The rules for scoring are as follows:
  • [0305]
    if 4 of the last 4 expected pulses were seen, score=high
  • [0306]
    if 3 of the last 4 expected pulses were seen, score=med. high
  • [0307]
    if 2 of the last 4 expected pulses seen, score=medium
  • [0308]
    if 1 or 0 of the last 4 expected pulses seen, score=low
  • [0309]
    An array of inter-beat intervals is maintained in order to provide the averaging process the information it needs. In order to keep missing or extra pulses from skewing the averaging process, extra pulses are not recorded in the history array, and missing pulses are assumed present and are inserted into the history array.
  • [0310]
    The algorithm is simplified by recognizing the fact a maximum of one pulse can be seen or expected every time this process is run (8 times a second). A flowchart of this trend-tracking process is shown in FIG. 28.
  • [0000]
    Decide which Trend to Use (if any)
  • [0311]
    This process decides which set of inter-beat periods to use when calculating the heart rate. The scores generated by the “look for a new trend” and “track existing trend” processes indicate which array of historic inter-beat values are of higher quality, so the scores alone are the mechanism for making this decision. If both scores are the same, the historic data for the existing trend is used since it has a tighter tolerance on how much an inter-beat interval can change from beat to beat.
  • [0312]
    Since a good score will not always be available from either or both trend analysis processes, this process has two additional modes of operation. First, if both the trend the trend tracking and acquisition processes have low scores, the heart rate status is set to “unstable”. Second, it there are no heartbeats but the ECG contacts are determined to be on-body, then the heart rate status is set to “none”.
  • [0313]
    If neither trend contains useful information, this trend selection process makes two key decisions. First, if there have not been any heart beats in a while, the heart rate is set to zero. A timer is managed in the “low pass filter and noise cancellation” process that is cleared when an ECG pulse is detected and incremented when no pulse is seen. Since that process is run every ⅛ second, the “no pulse” timer therefore counts at an 8 Hz rate. If the count exceeds a certain threshold, the pulse rate is set to zero and the rest of the trend selection process is skipped. Second, if either of the trend tracking processes has a low score and it is indicating missing pulses, the heart rate is set to a “slow heart rate” status.
  • Averaging Filter
  • [0314]
    The averaging filter works by looking at the previous 4 to 6 seconds of inter-beat timing intervals. Faster heart rates will therefore be averaged over a larger number of beats than slower rates, but even at a low-end 30 BPM heart rate, three pulses can averaged in a 6-second window.
  • [0315]
    The algorithm simply looks back in time through an array of historic inter-beat intervals until it sees at least 4 seconds of pulse timing, and then averages this most recent pulse timing. The filter is run once every two seconds, so updated averaged hear rates are available every two seconds. Since only the most recent “good” inter-beat intervals are used in the formula, missed pulses will not have an impact on the algorithms ability to generate new averages every 2 seconds.
  • [0316]
    The coarseness of the 32 Hz timebase does not compromise accuracy as long as inter-beat intervals extend over a 4 second period of time. Each of the inter-beat intervals in the historic array of values taken alone is not very accurate, but when added together, their round-off inaccuracies cancel out.
  • [0317]
    A sample averaging scenario is shown in FIG. 29. The heart rate is about 55 BPM, which corresponds to an inter-beat interval of about 35 counts (since the timer is running at 32 Hz). The most recent inter-beat interval is seen to be 34. Moving back far enough to get at least 4 seconds of heart beats (4×32=128 total counts) takes one back to an inter-beat interval of 33. The average counts is therefore (34+36+35+33)/4=34.5 counts.
  • [0318]
    A final low pass filter stage is added that limits how fast the heart rate can change. This is present to reflect the realities of physiology. A large step change in heart rate could imply an error in the new heart rate, so the rate at which the heart rate that is shared with the outside world is allowed to approach the calculated heart rate based on the old and new trends is limited to 4 BPM per second. As an example, if the previous heart rate was 60 and the newly calculated heart rate is 72, the heart rate sent out of the Life Signs Detection System will be 64, then 68 one second later, then 72 a second after that.
  • [0319]
    The math required to convert the filtered (averaged) inter-beat interval to a beats-per-second heart rate is simple. Since there are 32 clock ticks per second, the heart rate is (32/avg. inter-beat interval)*60. For the example above, this works out to (32/34.5)* 60=55 BPM.
  • Remote Communication
  • [0320]
    A sensor in the Life Signs Detection System (LSDS) communicates with a health hub (some kind of PC).
  • [0321]
    The wireless network connects a single sensor to a health hub via a receiver. The range is preferred to allow for reliable operation at 20 feet. The receiver manages the decoding of the data stream being received from the sensor. The link from the receiver to the health hub is simple serial RS232 at a 9600 baud rate as shown in FIG. 30. The health hub may be any small device having a processor, preferably a PC (desktop or laptop) or a PDA.
  • Physical Layer
  • [0322]
    A simple, low cost RF transceiver operating at 900 MHz may be used at both ends of the wireless link. Versions using transceivers would be capable of two-way communication.
  • Data Link Layer
  • [0323]
    Low cost RF modules tend to have two specific problems. First, as they are susceptible to noise, particularly in the absence of a transmitted signal, receivers tend to have noise pulses at random times. This means that the RF modules are not suited for sending asynchronous data. Second, the RF modules appear unable to hold a “high” level for longer than 10 or 15 milliseconds. This is most likely due to AGC circuitry. The modules therefore seem more content to see constantly changing data.
  • [0000]
    One solution to these two shortcomings is to Manchester encode the data being sent through the RF channel. This not only forces the data to change very often, but it reduces the sensitivity to noise. The bit rate will be 1 msec, and a “0” is encoded as a rising edge (01) in the middle of the bit time, and a “1” is encoded as a falling edge (10). An example bit stream is shown in FIG. 31:
  • [0324]
    In order for the receiver to recover the timing of the bit stream and hence understand when the start and middle of the bit time is, the transmitter must precede the actual data packet with a series of all 0's. The receiver will recover the bit timing by looking for consistent falling edges. The exact number of 0's in this leader is not important as long as it generates enough clock edges for the receiver to lock onto it. Eight to 16 bits should be fine. An all 0's leader corresponds to the encoded data stream shown in FIG. 32:
  • [0325]
    FIG. 48 is block diagram of the communication system of the present invention. The LSDS system preferably comprises a Body Area Network (BAN), a Personal Area Network (PAN), and a Local Area Network (LAN) or Wide Area Network (WAN). The BAN, in turn, comprises an LDS Monitor having a sensor array and software. The software provides signal processing to reduce signal noise and also provides the health state assessment algorithm together with its primary rule set. Within the BAN is also located a Communications Gateway comprising a wireless transmitter, data storage and query response software together with an optional wireless receiver and an on-board display.
  • [0326]
    The Communications Gateway enables two way communication with both the PAN and the LAN or WAN. The PAN comprises a Local Clinicain/Caregiver Unit, that may be implemented in a PDA or pager. It provides a multi-patient wireless transceiver, color screen (for color-code health state data), sound module (for alarm tones) and an optional input module (for clinician annotation). The PAN further preferably comprises software having a health state assessment algorithm with optional second tier rule sets, a multi-patient alarm display, a single-patient review/annotation module, an optional internet connection module and device query software (for addressing individual devices).
  • [0327]
    The LAN or WAN comprises a Remote Assessment/Dispatch Station that provides data storage connectivity with typically patient records or a full disclosure database and optionally a wide area alarm system. It further comprises software including a health assessment algorithm, optional clinical analysis software, a system overview module to display the status of all active monitors, a single patient detail/analysis/query module, and several further optional features. Among these optional features are an interface to Medic Dispatch Decision Software, to Emergency Response Databases, to Emergency Room Management Software, and to Disaster Management Systems.
  • Application Layer
  • [0328]
    Physiological information is transmitted out of the sensor on a periodic basis. This information is sent in packets in order to provide error detection and noise immunity. The packet format is:
  • [0329]
    [leader] [header1] [header2] [data] [checksum]
  • [0000]
    The header is a 16-bit pattern that allows the receiver to identify the start of a valid packet. The 10-byte data field is a number of bytes that describe the physiological condition of the wearer of the sensor. Lastly, the checksum is a 16-bit code that helps determine if the data was received without error. Header1 is 0x0d, while header2 is 0x1c. The 10-byte data field is encoded as follows:
  • [0330]
    byte 1=sensor ID (valid range is 1 through 250)
  • [0331]
    byte 2=health status, where
      • 0=black (no sensor data available)
      • 1=blue (uncertain or unreliable data
      • 2=red (health is very poor)
      • 3=yellow (health is marginal)
      • 4=green (A-OK)
  • [0337]
    byte 3=activity level, where
      • 0=no activity
      • 1=low level of activity (e.g. slow rolling, deep knee bends)
      • 2=medium level of activity (e.g. walking)
      • 3=high level of activity (e.g. running)
  • [0342]
    byte 4=temperature, signed integer, in degrees C.
  • [0343]
    byte 5=heart rate, where
      • 0 through 240=heart rate, in beats per minute
      • 250=strap leads off
      • 251=strap leads on, but cannot determine heart rate due to too many extra pulses (e.g. noise)
      • 252=strap leads on, but cannot determine heart rate due to too many missing pulses (e.g. ECG signal level too low)
  • [0348]
    byte 6=battery voltage status, where
      • 0=high
      • 1=moderate
      • 2=low
  • [0352]
    byte 7=orientation, where
      • upper 4 bits=up/down axis, where
        • 0000=not stable enough to determine orientation
        • 0001=upright
        • 0010=slanted
        • 0011=horizontal
        • 0100=inverted
      • lower 4 bits=side-to-side axis
      • byte 8=respiration rate, where
        • 0 through 100=respiration rate, in breaths per minute
        • 250=cannot determine respiration rate (e.g. too much-noise)
      • byte 9=confidence score of the overall health state assessment
      • byte 10=spare byte available for debug, testing, or future use
  • [0365]
    The checksum is a 16-bit summation of each of the data bytes. The summing is done byte-wide, but the result is 16-bIts wide.
  • [0366]
    All multi-byte entities are transmitted little-endian (lowest byte first). The only data that is affected by this rule is the 16-bit checksum since all other protocol elements are bytes. Orientation is interpreted using the diagram of FIG. 33.
  • [0000]
    TABLE 1
    LSDS Platform Parameters and Error Conditions
    Primary Life Sign Error Conditions
    Sensor Parameter Additional Data from Sensor
    R-Wave Detector HR Presence of signal (Yes or Leads Off
    No) Noisy Lead
    Heart rate variability Signal not detected
    Out of range - high
    Out of range - low
    Sensor INOP
    Temperature Sensor Temp (an estimate of core body External body temperature Signal not detected
    temperature value based on Out of range - high
    External Body Temperature as Out of range - low
    affected by ambient Sensor INOP
    temperature)
    Accelerometer Speed of motion (None, Slow, Body Position Sensor INOP
    Medium, High, or Off-scale (Vertical/Upright,
    Shock) Vertical/Upside-down,
    Horizontal)
    Respiration Presence of Respiration (Yes or Respiration Rate Bad signal (voltage too
    No) Tidal volume indicator high or too low)
    Time since last breath No breath detected
    Presence of motion Out of range - high
    Out of range - low
    Sensor INOP
    Other Information Platform ID (device serial Time Stamp of data packet Low Battery
    from Sensor Platform number, or possibly soldier ID
    number)
  • [0000]
    TABLE 2
    Default Health State Classification Descriptions
    Color
    Overall Health State Code
    Alive ♡ Green
    Alive, but significantly outside “normal” ♡ Yellow
    Dead ♡ Red
    Uncertain (Incomplete or conflicting information ♡ Blue
    from sensors)
    SENSOR PLATFORM NOT OPERATING ♡ Black
    (Determined by receiving platform, e.g., no
    data received at for a given prolonged interval)
  • [0000]
    TABLE 3
    Default Life Signs Interpretation Rules for Alive/Green and Dead/Red States
    Interpretation Rule
    Available Parameters Alive/Green Dead/Red
    HR only HR ≦ 160 BPM and HR ≧ 40 BPM HR = 0 for 4 minutes or more
    for 8 seconds or more HR < 30 BPM for 10 minutes or
    more
    RR only RR ≦ 30 breaths/minute and RR ≧ 8 RR = 0 for 5minutes or more
    breaths/minutes for 8 seconds or
    more
    Acceleration/Position only Insufficient to determine this state Insufficient to determine this state
    Temp only Insufficient to determine this state Insufficient to determine this state
    HR and RR [HR ≦ 160 BPM and HR ≧ 40 BPM HR = 0 and RR = 0 for 4 minutes
    and (RR ≦ 30 breaths/minute and ≧8 or more
    breaths/minutes)] for 8 seconds or
    more
    HR and Acceleration/Position (HR ≦ 160 BPM and HR ≧ 40 BPM) HR = 0 and Acceleration is NONE
    and any acceleration value (for any position value) for 4
    and any position value for 8 minutes or more
    seconds or more
    (HR > 160/BPM and HR ≦ 220 BPM)
    and (Acceleration is Medium
    or Fast for any Position value) for 8
    seconds or more
    HR and Temp (HR ≦ 160 BPM and HR ≧ 40 BPM) HR = 0 and Temp ≠ NORMAL for
    and (Temp = NORMAL) for 4 minutes or more
    8 seconds or more
    RR and Acceleration/Position RR ≦ 30 breaths/minute and RR ≧ 8 RR = 0 and Acceleration = NONE
    breaths/minutes and any (any Position value) for 5 minutes
    acceleration value and any position or more
    value for 8 seconds or more
    [(RR > 30 breaths per minute and
    RR ≦ 45 breaths per minute) and
    Acceleration is Fast, for any
    Position value)] for 8 seconds or
    more
    RR and Temp RR ≦ 30 breaths/minute and RR ≧ 8 RR = 0 and Temp ≠ NORMAL for
    breaths/minutes and Temp is 5 minutes or more
    NORMAL for 8 seconds or more
    Acceleration/Position and Temp Insufficient to determine this state Insufficient to determine this state
    HR, RR, and Acceleration/Position [(HR ≦ 160 BPM and HR ≧ 40 BPM) [(HR = 0) and (RR = 0) and
    and (RR ≦ 30 breaths/minute (Acceleration is NONE for any
    and RR ≧ 8 breaths/minutes) and Position value)] for 4 minutes or
    (any acceleration value and any more
    position value)] for 8 seconds or
    more
    [(HR > 160/BPM and HR ≦ 220 BPM)
    and (RR > 30 breaths per
    minute and RR ≦ 45 breaths per
    minute) and Acceleration is Fast,
    for any Position value)] for 8
    seconds or more
    HR, RR, and Temp [(HR ≦ 160 BPM and HR ≧ 40 BPM) [(HR = 0) and (RR = 0) and (any
    and (RR ≦ 30 breaths/minute Temp ≠ NORMAL)] for 4 minutes
    and RR ≧ 8 breaths/minutes) and or more
    (Temp is NORMAL)] for 8 seconds
    or more
    HR, Acceleration/Position and Temp [(HR ≦ 160 BPM and HR ≧ 40 BPM) [(HR = 0) and (Acceleration is
    and (any acceleration value NONE for any position value) and
    and any position value) and Temp Temp ≠ NORMAL)] for 4 minutes
    is NORMAL]for 8 seconds or more or more
    [(HR > 160/BPM and HR ≦ 220 BPM)
    and (RR > 30 breaths per
    minute and RR ≦ 45 breaths per
    minute) and (Acceleration is Fast,
    for any Position value) and Temp is
    NORMAL] for 8 seconds or more
    RR, Acceleration/Position and Temp [(RR ≦ 30 breaths/minute and RR ≧ [(RR = 0) and (Acceleration =
    8 breaths/minutes) and (any NONE for any Position value) and
    acceleration value and any position Temp ≠ NORMAL)] for 5 minutes
    value) and Temp is NORMAL] for or more
    8 seconds or more
    [(RR > 30 breaths per minute and
    RR ≦ 45 breaths per minute) and
    (Acceleration is Fast, for any
    Position value) and Temp is
    NORMAL] for 8 seconds or more
    HR, RR, Acceleration/Position and [(HR ≦ 160 BPM and HR ≧ 40 BPM) [(HR = 0) and (RR = 0) and
    Temp and (RR ≦ 30 breaths/minute (Acceleration is NONE for any
    and RR ≧ 8 breaths/minutes) and Position value) and Temp ≠
    (any acceleration value and any NORMAL] for 4 minutes or more
    position value) and Temp is
    NORMAL] for 8 seconds or more
    [(HR > 160/BPM and HR ≦ 220 BPM)
    and (RR > 30 breaths per
    minute and RR ≦ 45 breaths per
    minute) and Acceleration is Fast,
    for any Position value) and Temp is
    NORMAL] for 8 seconds or more
  • [0000]
    TABLE 4
    Default Life Signs Interpretation Rules for Alive/Yellow State
    Available Parameters Interpretation Rules
    HR only [(HR < 40 BPM and HR ≠ 0 BPM) or (HR > 160 BPM)] for 8 seconds or more
    RR only [(RR < 8 breaths/min and RR ≠ 0 breaths/min) or RR > 30 breaths/min)] for 8
    seconds or more
    Acceleration only Insufficient to determine this state
    Temp only Insufficient to determine this state
    HR and RR [(HR < 40 BPM and HR ≠ 0 BPM) and/or (HR > 160 BPM) or (RR < 8
    breaths/min and RR ≠ 0 breaths/min) or RR > 30 breaths/min)] for 8 seconds
    or more
    HR and Acceleration [(HR < 40 BPM and HR ≠ 0 BPM and any acceleration value) or (HR > 160 BPM)
    and Acceleration is NONE)] for 8 seconds or more
    HR and Temp [(HR < 40 BPM and HR ≠ 0 BPM for any Temp value) or (HR > 160 BPM
    and Acceleration < Medium for any Position value and any Temp value)] for
    8 seconds or more
    RR and Acceleration/Position [(RR < 8 breaths/min and RR ≠ 0 breaths/min and any acceleration value and
    any position value) or (RR > 30 breaths/min and Acceleration < Medium for
    any Position value)] for 8 seconds or more
    RR and Temp [(RR < 8 breaths/min and RR ≠ 0 breaths/min and any Temp value) or (RR >
    30 breaths/min and any Temp value)] for 8 seconds or more
    Acceleration/Position and Temp Insufficient to determine this state
    HR, RR, and Acceleration/Position [(HR < 40 BPM and HR ≠ 0 BPM) and/or (RR < 8 breaths/min and RR ≠ 0
    breaths/min) for any Acceleration value and any Position value] for 8 seconds
    or more
    [(HR > 160 BPM and/or RR > 30 breaths/min) and Acceleration < Medium for
    any position value] for 8 seconds or more
    HR, RR, and Temp [(HR < 40 BPM and HR ≠ 0 BPM) and/or (RR < 8 breaths/min and RR ≠ 0
    breaths/min) and any Temp value] for 8 seconds or more
    [(HR > 160 BPM and/or RR > 30 breaths/min) and any Temp value] for 8
    seconds or more
    HR, Acceleration/Position and Temp [(HR < 40 BPM and HR ≠ 0 BPM and any acceleration value and any Temp
    value) or (HR > 160 BPM and Acceleration < Medium and any Temp value)]
    for 8 seconds or more
    RR, Acceleration/Position and Temp [(RR < 8 breaths/min and RR ≠ 0 breaths/min and any acceleration value and
    any position value and any Temp value) or (RR > 30 breaths/min and
    Acceleration < Medium for any position value and any Temp value)] for 8
    seconds or more
    HR, RR, Acceleration/Position and [(HR < 40 BPM and HR ≠ 0 BPM) and/or (RR < 8 breaths/min and RR ≠ 0
    Temp breaths/min) for any Acceleration value and any Position value and any Temp
    value] for 8 seconds or more
    [(HR > 160 BPM and/or RR > 30 breaths/min) and Acceleration < Medium for
    any Position value and any Temp value] for 8 seconds or more
  • [0000]
    TABLE 5
    Default LSDS Alive/Normal Data Ranges
    Data Description “Normal”
    Sensor Parameter (Raw Data Range) Range
    R-Wave Heart Rate Numeric (0 BPM, 40-160 BPM
    Detector and 15-250 BPM)
    Presence of Heartbeat Boolean (T or F) TRUE
    Respiration Presence of Respiration Boolean (T or F) TRUE
    Detector Respiration Rate Numeric (0-60 breaths/min) 8-30 breaths/min
    Tidal Volume Indicator Integer (2, 1, 0) High, Medium
    (High, Medium, Low) or Low
    Time Elapsed Since Last Numeric Not applicable
    Breath (0-60 seconds)
    Presence of Motion Boolean (T or F) TRUE or
    FALSE
    Accelerometer Speed (None, Slow, Integer (0, 1, 2, or 3) 0-3
    Medium, Fast)
    Position (Upright, Signed Integer 0-1
    Horizontal, or Upside- (1, 0, or −1)
    Down)
    Temperature Estimated Core Numeric (0-50° C.) NORMAL
    sensor Temperature (36.4° C.-38.9° C.)
    External Temperature Numeric (0-50° C.) Not applicable
  • [0000]
    TABLE 6
    Default LSDS Alive/Not-Normal Data Ranges
    Parameter Abnormal High Abnormal Low
    HR 161 and higher 39 and lower
    RR 31 and higher 7 and lower
    Skin Temp >39° C. <36° C.
    Acceleration Not Applicable Not Applicable
    Position Not Applicable Not Applicable
  • [0000]
    TABLE 8
    Default Decision Matrix for Only One Parameter in Last Decision Interval
    New New New New
    Parameter Value State Value State Value State Value State
    HR Normal Alive Abnormal Alive- 0 BPM Dead Present, Uncertain
    Not- can't
    Normal calculate
    RR Normal Alive Abnormal Alive- 0 breaths Dead Present, Uncertain
    Not- per min can't
    Normal calculate
    Acceleration Any Uncertain
    Position Any Uncertain
    Temp Any Uncertain
  • [0000]
    TABLE 9
    Default Decision Matrix for Two Parameters in Last Decision Interval
    Average Average Average
    Value Value Value
    Parameters Range 1 Range 2 Range 3* New State
    HR and RR Normal Normal Alive
    Normal Abnormal Alive
    Normal 0 Alive/Not Normal
    Abnormal Normal Alive/Not Normal
    Abnormal Abnormal Alive/Not Normal
    Abnormal 0 Alive/Not Normal
    0 Normal Alive/Not Normal
    0 Abnormal Alive/Not Normal
    0 0 Dead
    HR and Normal Any Any Alive
    Acceleration/ Abnormal High Fast Any Alive
    Position Abnormal High Non-Fast Any Alive/Not Normal
    Abnormal Low None Any Alive/Not Normal
    Abnormal Low Non-zero Any Alive/Not Normal
    0 Any Any Dead
    HR and Normal Normal Alive
    Temp Normal H or L Alive/Not Normal
    Abnormal Normal Alive/Not Normal
    Abnormal H or L Alive/Not Normal
    0 Any Dead
    RR and Normal Any Any Alive
    Acceleration/ Abnormal High Fast Any Alive
    Position Abnormal High Non-Fast Any Alive/Not Normal
    Abnormal Low None Any Alive/Not Normal
    Abnormal Low Non-zero Any Uncertain
    0 Any Any Dead
    RR and Normal Normal Alive
    Temp Normal Abnormal Alive/Not Normal
    Abnormal Normal Alive/Not Normal
    Abnormal Abnormal Alive/Not Normal
    0 Normal Dead
    0 Abnormal Dead
    Temp and Any Any Any Uncertain
    Acceleration
    *Note that the third value range is only filled in for acceleration (acceleration and orientation).
  • [0000]
    TABLE 10
    Default Decision Matrix for Three Parameters for Last Decision Interval
    Average Average Average Average
    Value Value Value Value
    Parameters Range 1 Range 2 Range 3 Range 4* New State
    HR, RR, and Normal Normal Any Any Alive
    Acceleration Normal Abnormal Any Any Alive/Not Normal
    Normal 0 Any Any Alive/Not Normal
    Abnormal High Normal Any Any Alive/Not Normal
    Abnormal High Abnormal High Fast Any Alive
    Abnormal High Abnormal High Non-Fast Any Alive/Not Normal
    Abnormal High Abnormal Low Any Any Alive/Not Normal
    Abnormal High 0 Any Any Alive/Not Normal
    Abnormal Low Normal Any Any Alive/Not Normal
    Abnormal Low Abnormal Any Any Alive/Not Normal
    Abnormal Low 0 Any Any Alive/Not Normal
    0 Normal Any Any Alive/Not Normal
    0 Abnormal Any Any Alive/Not Normal
    0 0 Any Any Dead
    HR, RR, and Normal Normal Any Alive
    Temp Normal Abnormal Any Alive/Not Normal
    Normal 0 Any Alive/Not Normal
    Abnormal Normal Any Alive/Not Normal
    Abnormal Abnormal Any Alive/Not Normal
    Abnormal 0 Any Alive/Not Normal
    0 Normal Any Alive/Not Normal
    0 Abnormal Any Alive/Not Normal
    0 0 Any Dead
    HR, Temp, and Normal Normal Any Any Alive
    Acceleration Normal H or L Any Any Alive/Not Normal
    Abnormal High Normal Fast Any Alive
    Abnormal High Normal Non-Fast Any Alive/Not Normal
    Abnormal High Abnormal Any Any Alive/Not Normal
    Abnormal Low Any Any Any Alive/Not Normal
    0 Any Any Any Dead
    RR, Temp and Normal Normal Any Any Alive
    Acceleration Normal Abnormal Any Any Alive/Not Normal
    Abnormal High Normal Fast Any Alive
    Abnormal High Normal Non-Fast Any Alive/Not normal
    Abnormal High Abnormal Any Any Alive/Not Normal
    Abnormal Low Any Any Any Alive/Not Normal
    0 Any Any Any Dead
    *Note that the fourth value range is only filled in for acceleration (acceleration and orientation).
  • [0000]
    TABLE 11
    Default Decision Matrix for Four Parameters in Last Decision Interval
    Average Average Average Average Average
    Value Value Value Value Value
    Parameters Range 1 Range 2 Range 3 Range 4 Range 5 New State
    HR, RR, Temp and Normal Normal Normal Any Any Alive
    Acceleration Normal Normal Abnormal Any Any Alive
    Normal Abnormal Any Any Any Alive/Not
    Normal
    Normal 0 Any Any Any Alive/Not
    Normal
    Abnormal Normal *Any Any Any Alive/Not
    Normal
    Abnormal Abnormal Any Fast Any Alive
    High High
    Abnormal Abnormal Any Non-Fast Any Alive/Not
    High High Normal
    Abnormal Normal Any Any Any Alive/Not
    High Normal
    Abnormal Abnormal Any Any Any Alive/Not
    High Low Normal
    Abnormal 0 Any Any Any Alive/Not
    High Normal
    Abnormal Normal Any Any Any Alive/Not
    Low Normal
    Abnormal Abnormal Any Any Any Alive/Not
    Low Normal
    Abnormal 0 Any Any Any Alive/Not
    Low Normal
    0 Normal Any Any Any Alive/Not
    Normal
    0 Abnormal Any Any Any Alive/Not
    Normal
    0 0 Any Any Any Dead
    *Note that the fifth value range is only filled in for acceleration (acceleration and orientation).
  • [0000]
    TABLE 12
    State Change Score Components
    # of State State
    Change Total Change Influence on
    Steps Variations Probability Score Conf Score
    0 G
    Figure US20090131759A1-20090521-P00001
    G, Y
    Figure US20090131759A1-20090521-P00002
    Y, R
    Figure US20090131759A1-20090521-P00003
    R
    60% 3 H
    1 RH
    Figure US20090131759A1-20090521-P00004
    YH, YH
    Figure US20090131759A1-20090521-P00005
    G,
    30% 2 M
    G
    Figure US20090131759A1-20090521-P00006
    YL, YL
    Figure US20090131759A1-20090521-P00007
    RL
    2 or More G
    Figure US20090131759A1-20090521-P00008
    RH, G
    Figure US20090131759A1-20090521-P00009
    RL
    10% 1 L
  • [0000]
    TABLE 13
    Persistence Score Components
    Total # Times Score Range Influence on
    In New State (Total − 1) Conf Score
    7-8 6-7 H
    5-6 4-5 M
    4 3 L
  • [0000]
    TABLE 14
    Components of Weight (Multiplier) by Parameter Set
    Parameter Included in Weight Influence on
    New State (Multiplier) Conf Score
    All 1.0 H
    HR, RR, and Motion
    HR, RR, Temp 0.9 M
    HR and RR
    HR and Temp 0.8 L
    HR and Motion
    HR
    RR and Temp
    RR and Motion
    RR
  • [0000]
    TABLE 15
    Confidence Score Ranges
    Confidence Level Score Range
    High 80 < Score ≦ 100
    Medium 50 < Score ≦ 80
    Low Score < 50

Claims (54)

  1. 1. A life signs detection system for monitoring subjects, said system comprising a plurality of wearable platforms, each wearable platform comprising
    a sensor subsystem having a respiration rate sensor that detects abdominal motion of a subject
    a processor, and
    a transmitter for local sensor data of medical state information, a plurality of local hubs each comprising
    a separate wearable package comprising
    a local transceiver hub accepting connection from an external display and comprising
    a receiver for local sensor data from said wearable platforms,
    a remote base station receiving information from a plurality of local hubs and comprising said external display, and a rule processing engine comprising
    a processor executing a health state assessment algorithm that performs a medical evaluation and determines a confidence level for the evaluation, said algorithm comprising a rule set to calculate a health state classification and indicator of confidence.
  2. 2. The life signs detection system of claim 1 wherein the processing engine employs a subject personal baseline dependent rule set and tabulated parameter values.
  3. 3. The life signs detection system of claim 1 wherein the transmitter of the wearable platform is a short range RF transmitter having low bandwidth output for local sensor data.
  4. 4. The life signs detection system of claim 1 wherein the local transceiver hub comprises a short range RF transceiver, a medium or long range transmitter/transceiver and a processor.
  5. 5. The life signs detection system of claim 1 wherein said local sensor data comprises periodic and on demand digital data packets of medical state information from said wearable platforms.
  6. 6. The life signs detection system of claim 1 wherein said remote base station is a PDA.
  7. 7. The life signs detection system of claim 1 wherein said algorithm estimates the likelihood of injury.
  8. 8. The life signs detection system of claim 1 wherein said algorithm estimates the likelihood of an injury and the nature of the injury.
  9. 9. The life signs detection system of claim 1 wherein the processing engine employs a subject personal baseline dependent rule set.
  10. 10. The life signs detection system of claim 1 wherein said display comprises color coded health state classifications and decision confidence score.
  11. 11-43. (canceled)
  12. 44. A life signs detection system for monitoring one significant vital sign and one indirect life sign of subjects, said system comprising
    a plurality of wearable platforms, each wearable platform comprising
    a sensor subsystem comprising
    a heart rate sensor,
    a body motion sensor a respiration rate sensor, and
    a temperature sensor,
    wherein the respiration rate sensor detects motion of a subject,
    a processor, and
    a transmitter for local sensor data of medical state information,
    a plurality of local hubs each comprising
    a separate wearable package comprising
    a local transceiver hub accepting connection from an external display and comprising
    a receiver for local sensor data from said wearable platforms,
    a remote base station receiving information from a plurality of local hubs and comprising said external display, and
    a rule processing engine comprising
    a processor executing a health state assessment algorithm that performs a medical evaluation and determines a confidence level for the evaluation, said algorithm comprising a rule set to calculate a health state classification and indicator of confidence.
  13. 45. The life signs detection system of claim 44 wherein the processing engine employs a subject personal baseline dependent rule set and tabulated parameter values.
  14. 46. The life signs detection system of claim 44 wherein the respiration rate sensor detects abdominal motion of the subject.
  15. 47. The life signs detection system of claim 44 wherein said algorithm comprises tabulated interpretation rules and tabulated boundary conditions and tabulated abnormal values for each personal baseline.
  16. 48. The life signs detection system of claim 44 wherein the transmitter of the wearable platform is a short range RF transmitter having low bandwidth output for local sensor data.
  17. 49. The life signs detection system of claim 44 wherein the local transceiver hub comprises a short range RF transceiver, a medium or long range transmitter/transceiver and a processor.
  18. 50. The life signs detection system of claim 44 wherein said local sensor data comprises periodic and on demand digital data packets of medical state information from said wearable platforms.
  19. 51. The life signs detection system of claim 44 wherein said remote base station is a PDA.
  20. 52. The life signs detection system of claim 44 wherein said algorithm estimates the likelihood of injury.
  21. 53. The life signs detection system of claim 44 wherein said algorithm estimates the likelihood of an injury and the nature of the injury.
  22. 54. The life signs detection system of claim 44 wherein the processing engine employs a subject personal baseline dependent rule set.
  23. 55. The life signs detection system of claim 44 wherein said display comprises color coded health state classifications and decision confidence score.
  24. 56-92. (canceled)
  25. 93. A system for processing information on the physical status of one or more subjects comprising
    apparatus for transmitting information comprising
    a carrier for sensors arranged to be worn by the subjects for providing electrical signals including amplitude and duration values representative of physical parameters of the subjects, and
    a host receiver having a processor that determines whether the amplitude and duration values fall within acceptable limits.
  26. 94. The system for processing information on the physical status of one or more subjects of claim 93, having a communications protocol that assigns a set of sensors to a single hub, and a set of hubs to a single remote station.
  27. 95. The system for processing information on the physical status of one or more subjects of claim 94, wherein a local protocol provides the transport of data between one or more sensors and a single hub.
  28. 96. The system for processing information on the physical status of one or more subjects of claim 95, said system comprising a plurality of sensors, and wherein a local data packet format is extensible, not requiring changes to the hub to accommodate new sensor additions.
  29. 97. The system for processing information on the physical status of one or more subjects of claim 96, wherein gaps in the sensor data are accounted for by providing a filler packet, or by the indication that the sensor is no longer communicating.
  30. 98. The system for processing information on the physical status of one or more subjects of claim 96, wherein the filler packet comprises a timestamp.
  31. 99. The system for processing information on the physical status of one or more subjects of claim 98, wherein a distant protocol provides the transport of data between a hub, and the remote station.
  32. 100. The system for processing information on the physical status of one or more subjects of claim 99, wherein the distant protocol allows for interruptions in the data stream, with later recovery of data stored within the hub.
  33. 101. The system for processing information on the physical status of one or more subjects of claim 100, wherein the host receiver is comprised within a hub system that has a user interface that provides a local health display, wherein the host receiver further comprises a local selection mechanism to facilitate the initial association of one or more sensors to a specific hub.
  34. 102. The system for processing information on the physical status of one or more subjects of claim 93, wherein the association of a specific hub to a remote station is performed at the hub, or via a remote communications link, either to a medic PDA, or back to a remote station.
  35. 103. The system for processing information on the physical status of one or more subjects of claim 102 wherein the remote subsystem has a user interface that displays the-basic status of multiple hubs within a single display.
  36. 104. The system for processing information on the physical status of one or more subjects of claim 103, further comprising a display of status and data details from at least a single hub.
  37. 105. The system for processing information on the physical status of one or more subjects of claim 103, further comprising a medic PDA subsystem that has a user interface for displaying a list of hubs to connect to, and a mechanism to connect and display the detailed data as delivered by the hub.
  38. 106. The system for processing information on the physical status of one or more subjects of claim 93, wherein a running average of the amplitude and duration values of a group of previous respiration cycles is transmitted to the host processor, wherein a small hysteresis value is applied to the respiration signal to minimize false “end of cycle” readings due to noise in the signal, and wherein said hysteresis value is dynamically adjusted based on the amplitude of the previous cycle.
  39. 107-110. (canceled)
  40. 111. A method for transmitting information on the physical status of a subject comprising running an algorithm comprising the steps of
    looking for a new trend by
    looking at four most recent inter-beat intervals and
    developing a scoring based on the consistency of these intervals.
  41. 112. The method for transmitting information on the physical status of a subject of claim 111, further comprising using a window size+/−12.5%.
  42. 113. The method for transmitting information on the physical status of a subject of claim 111, wherein only consistent inter-beat intervals are saved in a history array.
  43. 114. The method for transmitting information on the physical status of a subject of claim 111, wherein an existing trend is tracked by
    assuming the heart rate to be at a certain frequency, and
    looking for more heartbeats at these expected intervals,
    ignoring extra pulses are ignored
    inserting missing pulses.
  44. 115. The method for transmitting information on the physical status of a subject of claim 111, wherein an existing trend process is locked onto a new trend when that new trend is seen to be strong and stable comprising
    maintaining a score for how well the trend is being tracked.
    unlocking the existing trend when its score is low, and then
    locking onto a new trend when the new trend is seen to exist.
  45. 116. The method for transmitting information on the physical status of a subject of claim 115, wherein an array of inter-beat intervals is maintained in order to provide the averaging process the information it needs.
  46. 117. The method for transmitting information on the physical status of a subject of claim 115, wherein if both the trend the trend tracking and acquisition processes have low scores, the heart rate status is set to “unstable”.
  47. 118. The method for transmitting information on the physical status of a subject of claim 115, wherein if there are no heartbeats but the EKG contacts are determined to be on-body, then the heart rate status is set to indicate “none”.
  48. 119. The method for transmitting information on the physical status of a subject of claim 115, wherein an averaging filter looks back in time through an array of historic inter-beat intervals until it sees at least 4 seconds of pulse timing, and then averages this most recent pulse timing.
  49. 120. The method for transmitting information on the physical status of a subject of claim 115, wherein a low pass filter stage limits how fast the heart rate can change, wherein, the rate at which the reported heart rate is allowed to approach the calculated heart rate based on the old and new trends is limited to 4 BPM per second.
  50. 121-138. (canceled)
  51. 139. A system for processing information on the physical status of one or more subjects comprising
    a sensor in carrier for sensors that communicates wirelessly with a health hub comprising a device having a processor.
  52. 140. The system for processing information on the physical status of one or more subjects of claim 139, further comprising a RF transceiver operating at the same frequency at both ends of the wireless link sending Manchester encoded data.
  53. 141. The system for processing information on the physical status of one or more subjects of claim 139, wherein the information is sent in packets with error correction bits.
  54. 142-148. (canceled)
US10595672 2003-11-04 2004-11-03 Life sign detection and health state assessment system Abandoned US20090131759A1 (en)

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Cited By (121)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20060258454A1 (en) * 2005-04-29 2006-11-16 Brick Todd A Advanced video controller system
US20070232867A1 (en) * 2006-04-01 2007-10-04 Draeger Medical Ag & Co. Kg Process and system for setting a patient monitor
US20070270671A1 (en) * 2006-04-10 2007-11-22 Vivometrics, Inc. Physiological signal processing devices and associated processing methods
US20080045815A1 (en) * 2006-06-20 2008-02-21 Derchak P A Automatic and ambulatory monitoring of congestive heart failure patients
US20080082018A1 (en) * 2003-04-10 2008-04-03 Sackner Marvin A Systems and methods for respiratory event detection
US20080139953A1 (en) * 2006-11-01 2008-06-12 Welch Allyn, Inc. Body worn physiological sensor device having a disposable electrode module
US20090006457A1 (en) * 2007-02-16 2009-01-01 Stivoric John M Lifeotypes
US20090018409A1 (en) * 2007-07-11 2009-01-15 Triage Wireless, Inc. Device for determining respiratory rate and other vital signs
US20090163774A1 (en) * 2007-12-20 2009-06-25 Sudeesh Thatha Managment and Diagnostic System for Patient Monitoring and Symptom Analysis
US20090305212A1 (en) * 2004-10-25 2009-12-10 Eastern Virginia Medical School System, method and medium for simulating normal and abnormal medical conditions
US20100063365A1 (en) * 2005-04-14 2010-03-11 Hidalgo Limited Apparatus and System for Monitoring
US20100062407A1 (en) * 2008-09-09 2010-03-11 Paul Jacques Charles Lecat Device And Methods For Medical Training Using Live Subjects
US20100160797A1 (en) * 2007-06-12 2010-06-24 Sotera Wireless, Inc. BODY-WORN SYSTEM FOR MEASURING CONTINUOUS NON-INVASIVE BLOOD PRESSURE (cNIBP)
US20100198509A1 (en) * 2007-06-07 2010-08-05 Qualcomm Incorporated 3d maps rendering device and method
US20100217158A1 (en) * 2009-02-25 2010-08-26 Andrew Wolfe Sudden infant death prevention clothing
US20100217345A1 (en) * 2009-02-25 2010-08-26 Andrew Wolfe Microphone for remote health sensing
US20100226491A1 (en) * 2009-03-09 2010-09-09 Thomas Martin Conte Noise cancellation for phone conversation
US20100234695A1 (en) * 2009-03-12 2010-09-16 Raytheon Company Networked symbiotic edge user infrastructure
US20100261982A1 (en) * 2007-12-06 2010-10-14 Norbert Noury Method and apparatus for detecting a critical situation of a subject
US20100274100A1 (en) * 2004-06-18 2010-10-28 Andrew Behar Systems and methods for monitoring subjects in potential physiological distress
US20100286545A1 (en) * 2009-05-06 2010-11-11 Andrew Wolfe Accelerometer based health sensing
US20100324388A1 (en) * 2009-06-17 2010-12-23 Jim Moon Body-worn pulse oximeter
US20110009710A1 (en) * 2007-01-30 2011-01-13 Brytech Inc. Combination level alarms and alarm persistence for patient monitoring
US20110066041A1 (en) * 2009-09-15 2011-03-17 Texas Instruments Incorporated Motion/activity, heart-rate and respiration from a single chest-worn sensor, circuits, devices, processes and systems
US20110066081A1 (en) * 2009-09-14 2011-03-17 Hiroshi Goto Sensor-Based Health Monitoring System
WO2011039745A1 (en) * 2009-09-30 2011-04-07 Healthwatch Ltd. Continuous non-interfering health monitoring and alert system
US20110148641A1 (en) * 2009-12-21 2011-06-23 Electronics And Telecommunications Research Institute Apparatus for detecting survival status of living thing and method using the same
US8033996B2 (en) 2005-07-26 2011-10-11 Adidas Ag Computer interfaces including physiologically guided avatars
WO2011131723A1 (en) 2010-04-23 2011-10-27 Roche Diagnostics Gmbh Method for generating a medical network
US20110288445A1 (en) * 2010-05-18 2011-11-24 Erik Lillydahl Systems and methods for reducing subconscious neuromuscular tension including bruxism
US20120029375A1 (en) * 2010-08-02 2012-02-02 Welch Allyn, Inc. Respirations Activity and Motion Measurement Using Accelerometers
US20120029299A1 (en) * 2010-07-28 2012-02-02 Deremer Matthew J Physiological status monitoring system
US20120065476A1 (en) * 2010-09-09 2012-03-15 Kyuhyoung Choi Self-examination apparatus and method for self-examination
US20120220888A1 (en) * 2009-05-06 2012-08-30 Empire Technology Development Llc Snoring treatment
WO2012171033A1 (en) * 2011-06-10 2012-12-13 Aliphcom Spacial and temporal vector analysis in wearable devices using sensor data
WO2012170110A1 (en) * 2011-06-10 2012-12-13 Aliphcom Wearable device and platform for sensory input
US20120313746A1 (en) * 2011-06-10 2012-12-13 Aliphcom Device control using sensory input
US8446275B2 (en) 2011-06-10 2013-05-21 Aliphcom General health and wellness management method and apparatus for a wellness application using data from a data-capable band
US20130178702A1 (en) * 2011-05-30 2013-07-11 Olympus Medical Systems Corp. Antenna apparatus, antenna, antenna holder, and body-insertable apparatus system
US20130190577A1 (en) * 2010-10-07 2013-07-25 Swisstom Ag Sensor device for electrical impedance tomography imaging, electrical impedance tomography imaging instrument and electrical impedance tomography method
US8527038B2 (en) 2009-09-15 2013-09-03 Sotera Wireless, Inc. Body-worn vital sign monitor
US20130231574A1 (en) * 2006-05-24 2013-09-05 Bao Tran Fitness monitoring
US8545417B2 (en) 2009-09-14 2013-10-01 Sotera Wireless, Inc. Body-worn monitor for measuring respiration rate
US8565109B1 (en) 2010-01-29 2013-10-22 University Of Washington Through Its Center Of Commercialization Optimization of polling protocols in sensor networks
US20130300565A1 (en) * 2006-02-09 2013-11-14 Deka Products Limited Partnership Adhesive and Peripheral Systems and Methods for Medical Devices
US8585606B2 (en) 2010-09-23 2013-11-19 QinetiQ North America, Inc. Physiological status monitoring system
US20130310656A1 (en) * 2012-05-21 2013-11-21 Gukchan LIM Mobile terminal with health care function and method of controlling the mobile terminal
US8591411B2 (en) 2010-03-10 2013-11-26 Sotera Wireless, Inc. Body-worn vital sign monitor
US8594776B2 (en) 2009-05-20 2013-11-26 Sotera Wireless, Inc. Alarm system that processes both motion and vital signs using specific heuristic rules and thresholds
US8602997B2 (en) 2007-06-12 2013-12-10 Sotera Wireless, Inc. Body-worn system for measuring continuous non-invasive blood pressure (cNIBP)
US8628480B2 (en) 2005-05-20 2014-01-14 Adidas Ag Methods and systems for monitoring respiratory data
US8672854B2 (en) 2009-05-20 2014-03-18 Sotera Wireless, Inc. System for calibrating a PTT-based blood pressure measurement using arm height
US20140088994A1 (en) * 2012-09-21 2014-03-27 CardioMEMS, Inc Method and system for trend-based patient management
US20140094677A1 (en) * 2012-10-02 2014-04-03 Seiko Instruments Inc. Biological information detecting apparatus and fixing structure
US20140123912A1 (en) * 2008-05-26 2014-05-08 PetPlace Ltd. Pet Animal Collar for Health & Vital Signs Monitoring, Alert and Diagnosis
US8747330B2 (en) 2010-04-19 2014-06-10 Sotera Wireless, Inc. Body-worn monitor for measuring respiratory rate
US8762733B2 (en) 2006-01-30 2014-06-24 Adidas Ag System and method for identity confirmation using physiologic biometrics to determine a physiologic fingerprint
US8795138B1 (en) 2013-09-17 2014-08-05 Sony Corporation Combining data sources to provide accurate effort monitoring
US8814792B2 (en) 2010-07-27 2014-08-26 Carefusion 303, Inc. System and method for storing and forwarding data from a vital-signs monitor
US20140266780A1 (en) * 2011-06-10 2014-09-18 Aliphcom Motion profile templates and movement languages for wearable devices
US8840549B2 (en) 2006-09-22 2014-09-23 Masimo Corporation Modular patient monitor
US8888700B2 (en) 2010-04-19 2014-11-18 Sotera Wireless, Inc. Body-worn monitor for measuring respiratory rate
US8968195B2 (en) 2006-05-12 2015-03-03 Bao Tran Health monitoring appliance
US8979765B2 (en) 2010-04-19 2015-03-17 Sotera Wireless, Inc. Body-worn monitor for measuring respiratory rate
US9017255B2 (en) 2010-07-27 2015-04-28 Carefusion 303, Inc. System and method for saving battery power in a patient monitoring system
US9028405B2 (en) 2006-05-16 2015-05-12 Bao Tran Personal monitoring system
WO2015080701A1 (en) * 2013-11-26 2015-06-04 Rivas Alvarez Victor Telemetric health monitoring devices and system
US9055925B2 (en) 2010-07-27 2015-06-16 Carefusion 303, Inc. System and method for reducing false alarms associated with vital-signs monitoring
US9060683B2 (en) 2006-05-12 2015-06-23 Bao Tran Mobile wireless appliance
US9069380B2 (en) 2011-06-10 2015-06-30 Aliphcom Media device, application, and content management using sensory input
US9113832B2 (en) 2002-03-25 2015-08-25 Masimo Corporation Wrist-mounted physiological measurement device
US9153112B1 (en) 2009-12-21 2015-10-06 Masimo Corporation Modular patient monitor
US9161696B2 (en) 2006-09-22 2015-10-20 Masimo Corporation Modular patient monitor
US9173594B2 (en) 2010-04-19 2015-11-03 Sotera Wireless, Inc. Body-worn monitor for measuring respiratory rate
US9173593B2 (en) 2010-04-19 2015-11-03 Sotera Wireless, Inc. Body-worn monitor for measuring respiratory rate
US9215980B2 (en) 2006-05-12 2015-12-22 Empire Ip Llc Health monitoring appliance
US20150366518A1 (en) * 2014-06-23 2015-12-24 Robert Sampson Apparatuses, Methods, Processes, and Systems Related to Significant Detrimental Changes in Health Parameters and Activating Lifesaving Measures
US20160029890A1 (en) * 2014-07-29 2016-02-04 Kurt Stump Computer-implemented systems and methods of automated physiological monitoring, prognosis, and triage
US9258670B2 (en) 2011-06-10 2016-02-09 Aliphcom Wireless enabled cap for a data-capable device
US9269119B2 (en) 2014-01-22 2016-02-23 Sony Corporation Devices and methods for health tracking and providing information for improving health
US20160051155A1 (en) * 2014-08-19 2016-02-25 Kuo-Yuan Chang Patient vital signs monitoring system and vital signs monitor
US20160058379A1 (en) * 2014-08-26 2016-03-03 PetPlace Ltd. Animal of Equidae Family Band or Collar for Health & Vital Signs Monitoring, Alert and Diagnosis
US9339209B2 (en) 2010-04-19 2016-05-17 Sotera Wireless, Inc. Body-worn monitor for measuring respiratory rate
US9357929B2 (en) 2010-07-27 2016-06-07 Carefusion 303, Inc. System and method for monitoring body temperature of a person
US9364158B2 (en) 2010-12-28 2016-06-14 Sotera Wirless, Inc. Body-worn system for continuous, noninvasive measurement of cardiac output, stroke volume, cardiac power, and blood pressure
US9408551B2 (en) 2013-11-14 2016-08-09 Bardy Diagnostics, Inc. System and method for facilitating diagnosis of cardiac rhythm disorders with the aid of a digital computer
US9408545B2 (en) 2013-09-25 2016-08-09 Bardy Diagnostics, Inc. Method for efficiently encoding and compressing ECG data optimized for use in an ambulatory ECG monitor
US9420952B2 (en) 2010-07-27 2016-08-23 Carefusion 303, Inc. Temperature probe suitable for axillary reading
US9433367B2 (en) 2013-09-25 2016-09-06 Bardy Diagnostics, Inc. Remote interfacing of extended wear electrocardiography and physiological sensor monitor
US9433380B1 (en) 2013-09-25 2016-09-06 Bardy Diagnostics, Inc. Extended wear electrocardiography patch
US9436645B2 (en) 2011-10-13 2016-09-06 Masimo Corporation Medical monitoring hub
US9439574B2 (en) 2011-02-18 2016-09-13 Sotera Wireless, Inc. Modular wrist-worn processor for patient monitoring
US9462975B2 (en) 1997-03-17 2016-10-11 Adidas Ag Systems and methods for ambulatory monitoring of physiological signs
US9504410B2 (en) 2005-09-21 2016-11-29 Adidas Ag Band-like garment for physiological monitoring
US9504423B1 (en) 2015-10-05 2016-11-29 Bardy Diagnostics, Inc. Method for addressing medical conditions through a wearable health monitor with the aid of a digital computer
US9526437B2 (en) 2012-11-21 2016-12-27 i4c Innovations Inc. Animal health and wellness monitoring using UWB radar
US20170010658A1 (en) * 2014-02-24 2017-01-12 Sony Corporation Smart wearable devices and methods with power consumption and network load optimization
US9545204B2 (en) 2013-09-25 2017-01-17 Bardy Diagnostics, Inc. Extended wear electrocardiography patch
US9545228B2 (en) 2013-09-25 2017-01-17 Bardy Diagnostics, Inc. Extended wear electrocardiography and respiration-monitoring patch
US9554715B2 (en) 2013-09-25 2017-01-31 Bardy Diagnostics, Inc. System and method for electrocardiographic data signal gain determination with the aid of a digital computer
US9585620B2 (en) 2010-07-27 2017-03-07 Carefusion 303, Inc. Vital-signs patch having a flexible attachment to electrodes
US9615792B2 (en) 2010-07-27 2017-04-11 Carefusion 303, Inc. System and method for conserving battery power in a patient monitoring system
US9619660B1 (en) 2013-09-25 2017-04-11 Bardy Diagnostics, Inc. Computer-implemented system for secure physiological data collection and processing
US9615763B2 (en) 2013-09-25 2017-04-11 Bardy Diagnostics, Inc. Ambulatory electrocardiography monitor recorder optimized for capturing low amplitude cardiac action potential propagation
US9655537B2 (en) 2013-09-25 2017-05-23 Bardy Diagnostics, Inc. Wearable electrocardiography and physiology monitoring ensemble
US9655538B2 (en) 2013-09-25 2017-05-23 Bardy Diagnostics, Inc. Self-authenticating electrocardiography monitoring circuit
USD788312S1 (en) 2012-02-09 2017-05-30 Masimo Corporation Wireless patient monitoring device
US9700223B2 (en) 2011-12-02 2017-07-11 Lumiradx Uk Ltd Method for forming a component of a wearable monitor
US9700227B2 (en) 2013-09-25 2017-07-11 Bardy Diagnostics, Inc. Ambulatory electrocardiography monitoring patch optimized for capturing low amplitude cardiac action potential propagation
US9706962B1 (en) * 2012-12-19 2017-07-18 Alert Core, Inc. Apparatus and method for teaching and algorithms for identifying qualifying movements
US9717432B2 (en) 2013-09-25 2017-08-01 Bardy Diagnostics, Inc. Extended wear electrocardiography patch using interlaced wire electrodes
US9717433B2 (en) 2013-09-25 2017-08-01 Bardy Diagnostics, Inc. Ambulatory electrocardiography monitoring patch optimized for capturing low amplitude cardiac action potential propagation
US9734304B2 (en) 2011-12-02 2017-08-15 Lumiradx Uk Ltd Versatile sensors with data fusion functionality
US9737224B2 (en) 2013-09-25 2017-08-22 Bardy Diagnostics, Inc. Event alerting through actigraphy embedded within electrocardiographic data
US9763581B2 (en) 2003-04-23 2017-09-19 P Tech, Llc Patient monitoring apparatus and method for orthosis and other devices
US9775536B2 (en) 2013-09-25 2017-10-03 Bardy Diagnostics, Inc. Method for constructing a stress-pliant physiological electrode assembly
US9833184B2 (en) 2006-10-27 2017-12-05 Adidas Ag Identification of emotional states using physiological responses
WO2017218907A1 (en) * 2016-06-16 2017-12-21 Arizona Board Of Regents On Behalf Of The University Of Arizona Systems, devices, and methods for determining an overall strength envelope
US9865176B2 (en) 2012-12-07 2018-01-09 Koninklijke Philips N.V. Health monitoring system
US9872087B2 (en) 2010-10-19 2018-01-16 Welch Allyn, Inc. Platform for patient monitoring
US9943269B2 (en) 2011-10-13 2018-04-17 Masimo Corporation System for displaying medical monitoring data

Families Citing this family (90)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7689437B1 (en) 2000-06-16 2010-03-30 Bodymedia, Inc. System for monitoring health, wellness and fitness
WO2002000111A1 (en) 2000-06-23 2002-01-03 Bodymedia, Inc. System for monitoring health, wellness and fitness
US20060122474A1 (en) 2000-06-16 2006-06-08 Bodymedia, Inc. Apparatus for monitoring health, wellness and fitness
DK1734858T3 (en) 2004-03-22 2014-10-20 Bodymedia Inc Non-invasive temperature monitoring device
US7020508B2 (en) 2002-08-22 2006-03-28 Bodymedia, Inc. Apparatus for detecting human physiological and contextual information
EP1667579A4 (en) 2003-09-12 2008-06-11 Bodymedia Inc Method and apparatus for measuring heart related parameters
GB2425180B (en) * 2005-04-14 2009-03-18 Justin Pisani Monitoring system
KR100738074B1 (en) * 2005-07-16 2007-07-10 삼성전자주식회사 Apparatus and method for managing health
US7760082B2 (en) * 2005-09-21 2010-07-20 Chon Meng Wong System and method for active monitoring and diagnostics of life signs using heartbeat waveform and body temperature remotely giving the user freedom to move within its vicinity without wires attachment, gel, or adhesives
WO2007038607A3 (en) * 2005-09-27 2007-07-05 Telzuit Technologies Llc Apparatus and method for monitoring patients
WO2007069111A3 (en) 2005-12-15 2007-12-13 Annelies Goris Device for assessing the physical condition of a person
WO2007101141A3 (en) * 2006-02-24 2008-01-10 Hmicro Inc A medical signal processing system with distributed wireless sensors
US20070203433A1 (en) * 2006-02-27 2007-08-30 Murphy Martin P Relaxation inducing apparatus
US8308641B2 (en) * 2006-02-28 2012-11-13 Koninklijke Philips Electronics N.V. Biometric monitor with electronics disposed on or in a neck collar
WO2007112527A3 (en) * 2006-03-31 2008-08-14 Alaide Pellegrini Mammana Devices constructive arrangement and methods applied to thoraxic cirtometry
US7629881B2 (en) 2006-04-28 2009-12-08 The Johns Hopkins University Sensor-based adaptive wearable devices and methods
CN101528128B (en) * 2006-10-06 2011-08-03 伊利克塔股份有限公司 Respiration sensor
US7611472B2 (en) * 2006-10-13 2009-11-03 Guixian Lu Apnea monitor
US20080275356A1 (en) * 2007-05-03 2008-11-06 Peter Stasz Respiratory sensing belt using piezo film
KR20080100658A (en) * 2007-05-14 2008-11-19 가톨릭대학교 산학협력단 Waistband with health management function
KR100903172B1 (en) * 2007-06-04 2009-06-17 충북대학교 산학협력단 Method for monitoring respiration in a wireless way and device for performing the same
DE102007044555A1 (en) * 2007-07-18 2009-01-22 Siemens Ag Optical coupling device and process for their preparation
US8206325B1 (en) 2007-10-12 2012-06-26 Biosensics, L.L.C. Ambulatory system for measuring and monitoring physical activity and risk of falling and for automatic fall detection
US8611319B2 (en) 2007-10-24 2013-12-17 Hmicro, Inc. Methods and apparatus to retrofit wired healthcare and fitness systems for wireless operation
US8628020B2 (en) 2007-10-24 2014-01-14 Hmicro, Inc. Flexible wireless patch for physiological monitoring and methods of manufacturing the same
US8251903B2 (en) 2007-10-25 2012-08-28 Valencell, Inc. Noninvasive physiological analysis using excitation-sensor modules and related devices and methods
WO2009074928A1 (en) * 2007-12-12 2009-06-18 Koninklijke Philips Electronics N.V. Measurement apparatus and method
US20090157113A1 (en) * 2007-12-18 2009-06-18 Ethicon Endo-Surgery, Inc. Wearable elements for implantable restriction systems
CN101969840B (en) * 2008-03-10 2013-04-03 皇家飞利浦电子股份有限公司 Watertight cardiac monitoring system
FI124646B (en) * 2008-06-11 2014-11-28 Suunto Oy The coupling and method for detecting the heart rate
US8231541B2 (en) * 2008-10-22 2012-07-31 Sharp Laboratories Of America, Inc. Asthma status scoring method and system with confidence ratings
KR101243763B1 (en) * 2008-12-18 2013-03-13 한국전자통신연구원 Apparatus and method for monitoring health index using electroconductive fiber
US8047083B2 (en) * 2009-02-17 2011-11-01 Black & Decker Corporation Trigger assembly including a flexible bend sensor
US8972197B2 (en) * 2009-09-15 2015-03-03 Numera, Inc. Method and system for analyzing breathing of a user
US8560267B2 (en) * 2009-09-15 2013-10-15 Imetrikus, Inc. Identifying one or more activities of an animate or inanimate object
US20100217533A1 (en) * 2009-02-23 2010-08-26 Laburnum Networks, Inc. Identifying a Type of Motion of an Object
US9470704B2 (en) 2009-02-23 2016-10-18 Nortek Security & Control Llc Wearable motion sensing device
US20110297155A1 (en) * 2009-02-25 2011-12-08 Koninklijke Philips Electronics N.V. Patient-ventilator dyssynchrony detection
EP3127476A1 (en) 2009-02-25 2017-02-08 Valencell, Inc. Light-guiding devices and monitoring devices incorporating same
US8788002B2 (en) 2009-02-25 2014-07-22 Valencell, Inc. Light-guiding devices and monitoring devices incorporating same
US9750462B2 (en) 2009-02-25 2017-09-05 Valencell, Inc. Monitoring apparatus and methods for measuring physiological and/or environmental conditions
DE102009001398A1 (en) * 2009-03-09 2010-09-16 Robert Bosch Gmbh Patch for detecting movements of a body
US20100245078A1 (en) * 2009-03-26 2010-09-30 Wellcore Corporation Wearable Motion Sensing Device
US20100262053A1 (en) * 2009-04-14 2010-10-14 Michael Gerard Crago Apparatus and method for a universal patient controlled medical binder
US20100268120A1 (en) * 2009-04-20 2010-10-21 Morten Eriksen Coil System and Method for Obtaining Volumetric Physiological Measurements
EP2263532A1 (en) * 2009-06-05 2010-12-22 Koninklijke Philips Electronics N.V. Motion determination apparatus
KR20110024205A (en) * 2009-09-01 2011-03-09 한국전자통신연구원 The non-intrusive wearable tidal volume measurement apparatus, system and method thereof
WO2011027942A1 (en) * 2009-09-04 2011-03-10 동아대학교 산학협력단 Capacitive respiration sensor, signal processing method of respiratory motion and apparatus therefor
KR101082210B1 (en) * 2009-09-04 2011-11-09 동아대학교 산학협력단 Capacitive breathing sensor, apparatus and method to detect breathing
US20110066063A1 (en) * 2009-09-11 2011-03-17 Compumedics Medical Innovation Pty Ltd. Respiratory Inductive Plethysmography Band
US8321004B2 (en) 2009-09-15 2012-11-27 Sotera Wireless, Inc. Body-worn vital sign monitor
WO2011034881A1 (en) * 2009-09-15 2011-03-24 Sotera Wireless, Inc. Body-worn vital sign monitor
US8364250B2 (en) 2009-09-15 2013-01-29 Sotera Wireless, Inc. Body-worn vital sign monitor
EP2536466A1 (en) * 2010-02-17 2012-12-26 Cardiac Pacemakers, Inc. Hemodynamic stability detection during arrhythmia using respiration sensor
US8979665B1 (en) 2010-03-22 2015-03-17 Bijan Najafi Providing motion feedback based on user center of mass
US20150150514A1 (en) 2010-08-06 2015-06-04 Conceptual Mindworks, Inc. Patient Care Recommendation System
US9924251B2 (en) * 2010-09-01 2018-03-20 Mor Efrati Transducer holder
KR20120057295A (en) * 2010-11-26 2012-06-05 한국전자통신연구원 The Non-intrusive wearable respiratory failure alarming apparatus and method thereof
EP2663230B1 (en) * 2011-01-12 2015-03-18 Koninklijke Philips N.V. Improved detection of breathing in the bedroom
US8753275B2 (en) * 2011-01-13 2014-06-17 BioSensics LLC Intelligent device to monitor and remind patients with footwear, walking aids, braces, or orthotics
GB201101858D0 (en) * 2011-02-03 2011-03-23 Isansys Lifecare Ltd Health monitoring
US9427191B2 (en) 2011-07-25 2016-08-30 Valencell, Inc. Apparatus and methods for estimating time-state physiological parameters
US9801552B2 (en) 2011-08-02 2017-10-31 Valencell, Inc. Systems and methods for variable filter adjustment by heart rate metric feedback
US9484013B1 (en) * 2012-02-20 2016-11-01 Mary Elizabeth McCulloch Speech simulation system
US9817440B2 (en) 2012-09-11 2017-11-14 L.I.F.E. Corporation S.A. Garments having stretchable and conductive ink
WO2014041032A1 (en) * 2012-09-11 2014-03-20 L.I.F.E. Corporation S.A. Wearable communication platform
US20150250420A1 (en) * 2014-03-10 2015-09-10 Gianluigi LONGINOTTI-BUITONI Physiological monitoring garments
EP2900128B1 (en) * 2012-09-26 2016-07-13 Laerdal Medical AS Pulse meter for new-borns
WO2014059405A1 (en) * 2012-10-12 2014-04-17 Inova Labs, Inc. Method and systems for the delivery of oxygen enriched gas
US9311789B1 (en) 2013-04-09 2016-04-12 BioSensics LLC Systems and methods for sensorimotor rehabilitation
US20140350883A1 (en) * 2013-05-10 2014-11-27 Abraham Carter Platform for Generating Sensor Data
US20140350355A1 (en) * 2013-05-27 2014-11-27 P-Tech HM Ltd. Monitoring and managing sleep breathing disorders
JP2016522049A (en) * 2013-05-31 2016-07-28 ナイキ イノベイト シーブイ Dynamic sampling
CN103559395A (en) * 2013-10-31 2014-02-05 浙江联众智慧科技股份有限公司 Life sign one-key input method
EP3116395A4 (en) * 2014-03-10 2017-12-06 L I F E Corp S A Physiological monitoring garments
EP2923642B1 (en) * 2014-03-25 2017-03-15 Ulrich Scholten Application agnostic sensor, control computer and methods for operating
EP3151748A4 (en) * 2014-06-03 2018-01-10 Myair Llc Breath volume monitoring system and method
JP2016010616A (en) 2014-06-30 2016-01-21 株式会社東芝 Respiration state estimation apparatus, respiration state estimation method, and program
US20160022212A1 (en) * 2014-07-24 2016-01-28 Dymedix Corporation Reusable respiratory effort sensor module
US20160029898A1 (en) 2014-07-30 2016-02-04 Valencell, Inc. Physiological Monitoring Devices and Methods Using Optical Sensors
US9794653B2 (en) 2014-09-27 2017-10-17 Valencell, Inc. Methods and apparatus for improving signal quality in wearable biometric monitoring devices
US9724003B2 (en) * 2014-11-14 2017-08-08 Intel Corporation Ultra-low power continuous heart rate sensing in wearable devices
EP3231305A1 (en) * 2014-12-12 2017-10-18 Timpel S.A. Fitting device for adjusting electrode belts
EP3232923A2 (en) * 2014-12-15 2017-10-25 Koninklijke Philips N.V. Respiration rate monitoring by multiparameter algorithm in a device including integrated belt sensor
US20160242992A1 (en) * 2015-02-23 2016-08-25 Norman A. Paradis System for dynamically stabilizing the chest wall after injury, fracture, or operative procedures
US9913583B2 (en) 2015-07-01 2018-03-13 Rememdia LC Health monitoring system using outwardly manifested micro-physiological markers
US20170020455A1 (en) * 2015-07-20 2017-01-26 King's Metal Fiber Technologies Co., Ltd. Structure of detective garment
CN106445101A (en) * 2015-08-07 2017-02-22 飞比特公司 Method and system for identifying user
US9757071B1 (en) * 2016-04-29 2017-09-12 Bayer Healthcare Llc System and method for suppressing noise from electrocardiographic (ECG) signals
US20180000417A1 (en) * 2016-06-29 2018-01-04 David R. Hall Toilet With A Health Monitoring Torso Belt

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6160478A (en) * 1998-10-27 2000-12-12 Sarcos Lc Wireless health monitoring system
US6198394B1 (en) * 1996-12-05 2001-03-06 Stephen C. Jacobsen System for remote monitoring of personnel
US20040242972A1 (en) * 2003-05-28 2004-12-02 General Electric Company Method, system and computer product for prognosis of a medical disorder
US20050177393A1 (en) * 2002-03-22 2005-08-11 Sacco William J. Method and system of rule-based triage
US7054784B2 (en) * 1994-11-21 2006-05-30 Phatrat Technology, Inc. Sport monitoring systems

Family Cites Families (29)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US3888240A (en) * 1974-02-08 1975-06-10 Survival Technology Electrode assembly and methods of using the same in the respiratory and/or cardiac monitoring of an infant
US4572197A (en) * 1982-07-01 1986-02-25 The General Hospital Corporation Body hugging instrumentation vest having radioactive emission detection for ejection fraction
US5007427A (en) * 1987-05-07 1991-04-16 Capintec, Inc. Ambulatory physiological evaluation system including cardiac monitoring
US4803625A (en) * 1986-06-30 1989-02-07 Buddy Systems, Inc. Personal health monitor
US4827943A (en) * 1986-09-23 1989-05-09 Advanced Medical Technologies, Inc. Portable, multi-channel, physiological data monitoring system
US5078134A (en) * 1988-04-25 1992-01-07 Lifecor, Inc. Portable device for sensing cardiac function and automatically delivering electrical therapy
US5047952A (en) * 1988-10-14 1991-09-10 The Board Of Trustee Of The Leland Stanford Junior University Communication system for deaf, deaf-blind, or non-vocal individuals using instrumented glove
US5235989A (en) * 1990-03-07 1993-08-17 Sleep Disorders Center Apparatus for sensing respiration movements
EP0448438A3 (en) * 1990-03-07 1991-12-04 The Sleep Disorders Diagnostic And Treatment Center Ltd. Device for sensing respiration movements
US5125412A (en) * 1990-07-23 1992-06-30 Thornton William E Musculoskeletal activity monitor
US5185841A (en) 1990-11-29 1993-02-09 Pirelli Cavi S.P.A. Optical fibre element comprising an optical fibre housing constituted by a polyolefin material, and an h2-absorbing buffer
US5353793A (en) * 1991-11-25 1994-10-11 Oishi-Kogyo Company Sensor apparatus
US5263491A (en) * 1992-05-12 1993-11-23 William Thornton Ambulatory metabolic monitor
NL9202256A (en) * 1992-12-24 1994-07-18 Peter Bernard Defares Interactive breathing regulator.
US6394811B2 (en) * 1997-03-20 2002-05-28 Terese Finitzo Computer-automated implementation of user-definable decision rules for medical diagnostic or screening interpretations
US5928157A (en) * 1998-01-22 1999-07-27 O'dwyer; Joseph E. Apnea detection monitor with remote receiver
US6385473B1 (en) * 1999-04-15 2002-05-07 Nexan Limited Physiological sensor device
US6494829B1 (en) * 1999-04-15 2002-12-17 Nexan Limited Physiological sensor array
US6454708B1 (en) * 1999-04-15 2002-09-24 Nexan Limited Portable remote patient telemonitoring system using a memory card or smart card
US7256708B2 (en) 1999-06-23 2007-08-14 Visicu, Inc. Telecommunications network for remote patient monitoring
US6470893B1 (en) * 2000-05-15 2002-10-29 Peter V. Boesen Wireless biopotential sensing device and method with capability of short-range radio frequency transmission and reception
EP1169966B1 (en) * 2000-07-06 2011-08-10 Esaote S.p.A. Method for ultrasound imaging in the presence of contrast agents in the field of cardiology
US6461307B1 (en) * 2000-09-13 2002-10-08 Flaga Hf Disposable sensor for measuring respiration
US8280682B2 (en) * 2000-12-15 2012-10-02 Tvipr, Llc Device for monitoring movement of shipped goods
US20020169584A1 (en) * 2001-05-14 2002-11-14 Zhongsu Fu Mobile monitoring system
US20040073460A1 (en) * 2002-10-01 2004-04-15 Erwin W. Gary Method for managing the healthcare of members of a population
US8043213B2 (en) * 2002-12-18 2011-10-25 Cardiac Pacemakers, Inc. Advanced patient management for triaging health-related data using color codes
WO2005032447A3 (en) * 2003-08-22 2006-03-09 Jeremy Bowman Physiological monitoring garment
US7740588B1 (en) * 2005-06-24 2010-06-22 Michael Sciarra Wireless respiratory and heart rate monitoring system

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7054784B2 (en) * 1994-11-21 2006-05-30 Phatrat Technology, Inc. Sport monitoring systems
US6198394B1 (en) * 1996-12-05 2001-03-06 Stephen C. Jacobsen System for remote monitoring of personnel
US6160478A (en) * 1998-10-27 2000-12-12 Sarcos Lc Wireless health monitoring system
US20050177393A1 (en) * 2002-03-22 2005-08-11 Sacco William J. Method and system of rule-based triage
US20040242972A1 (en) * 2003-05-28 2004-12-02 General Electric Company Method, system and computer product for prognosis of a medical disorder

Cited By (235)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9462975B2 (en) 1997-03-17 2016-10-11 Adidas Ag Systems and methods for ambulatory monitoring of physiological signs
US9750429B1 (en) 2000-04-17 2017-09-05 Adidas Ag Systems and methods for ambulatory monitoring of physiological signs
US9113832B2 (en) 2002-03-25 2015-08-25 Masimo Corporation Wrist-mounted physiological measurement device
US9788735B2 (en) 2002-03-25 2017-10-17 Masimo Corporation Body worn mobile medical patient monitor
US9872623B2 (en) 2002-03-25 2018-01-23 Masimo Corporation Arm mountable portable patient monitor
US9113831B2 (en) 2002-03-25 2015-08-25 Masimo Corporation Physiological measurement communications adapter
US9795300B2 (en) 2002-03-25 2017-10-24 Masimo Corporation Wearable portable patient monitor
US20080082018A1 (en) * 2003-04-10 2008-04-03 Sackner Marvin A Systems and methods for respiratory event detection
US9763581B2 (en) 2003-04-23 2017-09-19 P Tech, Llc Patient monitoring apparatus and method for orthosis and other devices
US20100274100A1 (en) * 2004-06-18 2010-10-28 Andrew Behar Systems and methods for monitoring subjects in potential physiological distress
US9492084B2 (en) * 2004-06-18 2016-11-15 Adidas Ag Systems and methods for monitoring subjects in potential physiological distress
US8882511B2 (en) * 2004-10-25 2014-11-11 Eastern Virginia Medical School System, method and medium for simulating normal and abnormal medical conditions
US20090305212A1 (en) * 2004-10-25 2009-12-10 Eastern Virginia Medical School System, method and medium for simulating normal and abnormal medical conditions
US20170049338A1 (en) * 2005-04-14 2017-02-23 Hidalgo Limited Apparatus and system for monitoring
US20130237772A1 (en) * 2005-04-14 2013-09-12 Hidalgo Limited Apparatus and system for monitoring
US20150313476A1 (en) * 2005-04-14 2015-11-05 Hildalgo Limited Apparatus and system for monitoring
US20100063365A1 (en) * 2005-04-14 2010-03-11 Hidalgo Limited Apparatus and System for Monitoring
US20060258454A1 (en) * 2005-04-29 2006-11-16 Brick Todd A Advanced video controller system
US8651964B2 (en) * 2005-04-29 2014-02-18 The United States Of America As Represented By The Secretary Of The Army Advanced video controller system
US8628480B2 (en) 2005-05-20 2014-01-14 Adidas Ag Methods and systems for monitoring respiratory data
US8790255B2 (en) 2005-07-26 2014-07-29 Adidas Ag Computer interfaces including physiologically guided avatars
US8033996B2 (en) 2005-07-26 2011-10-11 Adidas Ag Computer interfaces including physiologically guided avatars
US9504410B2 (en) 2005-09-21 2016-11-29 Adidas Ag Band-like garment for physiological monitoring
US8762733B2 (en) 2006-01-30 2014-06-24 Adidas Ag System and method for identity confirmation using physiologic biometrics to determine a physiologic fingerprint
US9259531B2 (en) * 2006-02-09 2016-02-16 Deka Products Limited Partnership Adhesive and peripheral systems and methods for medical devices
US20130300565A1 (en) * 2006-02-09 2013-11-14 Deka Products Limited Partnership Adhesive and Peripheral Systems and Methods for Medical Devices
US20070232867A1 (en) * 2006-04-01 2007-10-04 Draeger Medical Ag & Co. Kg Process and system for setting a patient monitor
US20070270671A1 (en) * 2006-04-10 2007-11-22 Vivometrics, Inc. Physiological signal processing devices and associated processing methods
US9820657B2 (en) 2006-05-12 2017-11-21 Koninklijke Philips N.V. Mobile wireless appliance
US8968195B2 (en) 2006-05-12 2015-03-03 Bao Tran Health monitoring appliance
US9060683B2 (en) 2006-05-12 2015-06-23 Bao Tran Mobile wireless appliance
US9215980B2 (en) 2006-05-12 2015-12-22 Empire Ip Llc Health monitoring appliance
US9028405B2 (en) 2006-05-16 2015-05-12 Bao Tran Personal monitoring system
US20140249429A1 (en) * 2006-05-24 2014-09-04 Bao Tran Fitness monitoring
US9107586B2 (en) * 2006-05-24 2015-08-18 Empire Ip Llc Fitness monitoring
US8764651B2 (en) * 2006-05-24 2014-07-01 Bao Tran Fitness monitoring
US20130231574A1 (en) * 2006-05-24 2013-09-05 Bao Tran Fitness monitoring
US20080045815A1 (en) * 2006-06-20 2008-02-21 Derchak P A Automatic and ambulatory monitoring of congestive heart failure patients
US8475387B2 (en) 2006-06-20 2013-07-02 Adidas Ag Automatic and ambulatory monitoring of congestive heart failure patients
US8840549B2 (en) 2006-09-22 2014-09-23 Masimo Corporation Modular patient monitor
US9161696B2 (en) 2006-09-22 2015-10-20 Masimo Corporation Modular patient monitor
US9833184B2 (en) 2006-10-27 2017-12-05 Adidas Ag Identification of emotional states using physiological responses
US8214007B2 (en) 2006-11-01 2012-07-03 Welch Allyn, Inc. Body worn physiological sensor device having a disposable electrode module
US8630699B2 (en) 2006-11-01 2014-01-14 Welch Allyn, Inc. Body worn physiological sensor device having a disposable electrode module
US9155484B2 (en) 2006-11-01 2015-10-13 Welch Allyn, Inc. Body worn physiological sensor device having a disposable electrode module
US9877663B2 (en) 2006-11-01 2018-01-30 Welch Allyn, Inc. Body worn physiological sensor device having a disposable electrode module
US9433366B2 (en) 2006-11-01 2016-09-06 Welch Allyn, Inc. Body worn physiological sensor device having a disposable electrode module
US20080139953A1 (en) * 2006-11-01 2008-06-12 Welch Allyn, Inc. Body worn physiological sensor device having a disposable electrode module
US8750974B2 (en) 2006-11-01 2014-06-10 Welch Allyn, Inc. Body worn physiological sensor device having a disposable electrode module
US8965492B2 (en) 2006-11-01 2015-02-24 Welch Allyn, Inc. Body worn physiological sensor device having a disposable electrode module
US20110009710A1 (en) * 2007-01-30 2011-01-13 Brytech Inc. Combination level alarms and alarm persistence for patient monitoring
US20140221775A1 (en) * 2007-02-16 2014-08-07 Bodymedia, Inc. Delivering content based on a determination of stress
US20140222735A1 (en) * 2007-02-16 2014-08-07 Bodymedia, Inc. Systems, methods, and devices to determine an individuals mood
US20140220525A1 (en) * 2007-02-16 2014-08-07 Bodymedia, Inc. Managing educational content based on detected stress state and an individuals predicted type
US20140222849A1 (en) * 2007-02-16 2014-08-07 Bodymedia, Inc. Adaptation of user-interface based on predicted individual type
US20140221730A1 (en) * 2007-02-16 2014-08-07 Bodymedia, Inc. Delivering content based on an individuals predicted type and stress-related state
US20140344282A1 (en) * 2007-02-16 2014-11-20 Bodymedia, Inc. Systems, methods and devices for determining sleep quality with wearable devices
US20140317135A1 (en) * 2007-02-16 2014-10-23 Bodymedia, Inc. Providing recommendations to individuals based on the individuals type
US20140222732A1 (en) * 2007-02-16 2014-08-07 Bodymedia, Inc. Managing educational content based on detected stress state
US20140221773A1 (en) * 2007-02-16 2014-08-07 Bodymedia, Inc. Determining a continuous duration that an individual has been in the stress-related state
US20140317042A1 (en) * 2007-02-16 2014-10-23 Bodymedia, Inc. Systems, methods, and devices utilizing cumulitive sleep data to predict the health of an individual
US20140214874A1 (en) * 2007-02-16 2014-07-31 Bodymedia, Inc. Predicted type and contexts in assessments
US20090006457A1 (en) * 2007-02-16 2009-01-01 Stivoric John M Lifeotypes
US20140309939A1 (en) * 2007-02-16 2014-10-16 Bodymedia, Inc. Systems and methods using an individual's context and type to determine the individuals status
US20140214836A1 (en) * 2007-02-16 2014-07-31 Bodymedia, Inc. Systems and methods using an individuals predicted type and context for behavioral modification
US20140122537A1 (en) * 2007-02-16 2014-05-01 Bodymedia, Inc. Using aggregated sensed data of individuals to predict physiological state
US20140310223A1 (en) * 2007-02-16 2014-10-16 Bodymedia, Inc. Systems and methods making recommendations based on data from wearable devices
US20140310297A1 (en) * 2007-02-16 2014-10-16 Bodymedia, Inc. Home automation systems utilizing detected stress data of an individual and the individuals predicted type
US20140308639A1 (en) * 2007-02-16 2014-10-16 Bodymedia, Inc. Data from wearable sensor devices to manage educational content
US20140310274A1 (en) * 2007-02-16 2014-10-16 Bodymedia, Inc. Using a wearable device to predict an individuals type and response to content
US20140122536A1 (en) * 2007-02-16 2014-05-01 Bodymedia, Inc. Methods for behavior modification based on data from a wearable device
US20140122496A1 (en) * 2007-02-16 2014-05-01 Bodymedia, Inc. Using data from wearable monitors to identify cohorts
US20140308636A1 (en) * 2007-02-16 2014-10-16 Bodymedia, Inc. Providing recommendations based on detected stress and a predicted type for an individual
US20140214873A1 (en) * 2007-02-16 2014-07-31 Bodymedia, Inc. Using individuals predicted type to aid in content selection
US20140310294A1 (en) * 2007-02-16 2014-10-16 Bodymedia, Inc. Systems and methods using a wearable device to predict the individuals type and a suitable therapy regime
US20140310284A1 (en) * 2007-02-16 2014-10-16 Bodymedia, Inc. Generation of content based on predicted individual type
US20140310105A1 (en) * 2007-02-16 2014-10-16 Bodymedia, Inc. Systems, methods, and devices to determine an individuals mood based on sensed data and the individuals predicted type
US20140180025A1 (en) * 2007-02-16 2014-06-26 Bodymedia, Inc. System and method of predicting the type of individual used with separate applications
US20140180024A1 (en) * 2007-02-16 2014-06-26 Bodymedia, Inc. Systems and methods using a wearable device to predict an individuals daily routine
US20140310296A1 (en) * 2007-02-16 2014-10-16 Bodymedia, Inc. Systems and methods making recommendations based on data from wearable devices
US20140180720A1 (en) * 2007-02-16 2014-06-26 Bodymedia, Inc. Data predicting the type of individual used with separate applications
US20140317039A1 (en) * 2007-02-16 2014-10-23 Bodymedia, Inc. Systems, methods, and devices utilizing cumulative sleep data to predict the health of an individual
US20100198509A1 (en) * 2007-06-07 2010-08-05 Qualcomm Incorporated 3d maps rendering device and method
US20100160797A1 (en) * 2007-06-12 2010-06-24 Sotera Wireless, Inc. BODY-WORN SYSTEM FOR MEASURING CONTINUOUS NON-INVASIVE BLOOD PRESSURE (cNIBP)
US8740802B2 (en) 2007-06-12 2014-06-03 Sotera Wireless, Inc. Body-worn system for measuring continuous non-invasive blood pressure (cNIBP)
US8808188B2 (en) 2007-06-12 2014-08-19 Sotera Wireless, Inc. Body-worn system for measuring continuous non-invasive blood pressure (cNIBP)
US8602997B2 (en) 2007-06-12 2013-12-10 Sotera Wireless, Inc. Body-worn system for measuring continuous non-invasive blood pressure (cNIBP)
US9215986B2 (en) 2007-06-12 2015-12-22 Sotera Wireless, Inc. Body-worn system for measuring continuous non-invasive blood pressure (cNIBP)
US9161700B2 (en) 2007-06-12 2015-10-20 Sotera Wireless, Inc. Body-worn system for measuring continuous non-invasive blood pressure (cNIBP)
US9668656B2 (en) * 2007-06-12 2017-06-06 Sotera Wireless, Inc. Body-worn system for measuring continuous non-invasive blood pressure (cNIBP)
US8506480B2 (en) * 2007-07-11 2013-08-13 Sotera Wireless, Inc. Device for determining respiratory rate and other vital signs
US20090018409A1 (en) * 2007-07-11 2009-01-15 Triage Wireless, Inc. Device for determining respiratory rate and other vital signs
US20100261982A1 (en) * 2007-12-06 2010-10-14 Norbert Noury Method and apparatus for detecting a critical situation of a subject
US20090163774A1 (en) * 2007-12-20 2009-06-25 Sudeesh Thatha Managment and Diagnostic System for Patient Monitoring and Symptom Analysis
US20140123912A1 (en) * 2008-05-26 2014-05-08 PetPlace Ltd. Pet Animal Collar for Health & Vital Signs Monitoring, Alert and Diagnosis
US20100062407A1 (en) * 2008-09-09 2010-03-11 Paul Jacques Charles Lecat Device And Methods For Medical Training Using Live Subjects
US8866621B2 (en) 2009-02-25 2014-10-21 Empire Technology Development Llc Sudden infant death prevention clothing
US20100217158A1 (en) * 2009-02-25 2010-08-26 Andrew Wolfe Sudden infant death prevention clothing
US20100217345A1 (en) * 2009-02-25 2010-08-26 Andrew Wolfe Microphone for remote health sensing
US8882677B2 (en) 2009-02-25 2014-11-11 Empire Technology Development Llc Microphone for remote health sensing
US8628478B2 (en) 2009-02-25 2014-01-14 Empire Technology Development Llc Microphone for remote health sensing
US20100226491A1 (en) * 2009-03-09 2010-09-09 Thomas Martin Conte Noise cancellation for phone conversation
US8824666B2 (en) 2009-03-09 2014-09-02 Empire Technology Development Llc Noise cancellation for phone conversation
US9596989B2 (en) * 2009-03-12 2017-03-21 Raytheon Company Networked symbiotic edge user infrastructure
US20100234695A1 (en) * 2009-03-12 2010-09-16 Raytheon Company Networked symbiotic edge user infrastructure
US8836516B2 (en) * 2009-05-06 2014-09-16 Empire Technology Development Llc Snoring treatment
US20120220888A1 (en) * 2009-05-06 2012-08-30 Empire Technology Development Llc Snoring treatment
US20100286545A1 (en) * 2009-05-06 2010-11-11 Andrew Wolfe Accelerometer based health sensing
US8956294B2 (en) 2009-05-20 2015-02-17 Sotera Wireless, Inc. Body-worn system for continuously monitoring a patients BP, HR, SpO2, RR, temperature, and motion; also describes specific monitors for apnea, ASY, VTAC, VFIB, and ‘bed sore’ index
US8594776B2 (en) 2009-05-20 2013-11-26 Sotera Wireless, Inc. Alarm system that processes both motion and vital signs using specific heuristic rules and thresholds
US8738118B2 (en) 2009-05-20 2014-05-27 Sotera Wireless, Inc. Cable system for generating signals for detecting motion and measuring vital signs
US8956293B2 (en) 2009-05-20 2015-02-17 Sotera Wireless, Inc. Graphical ‘mapping system’ for continuously monitoring a patient's vital signs, motion, and location
US8909330B2 (en) 2009-05-20 2014-12-09 Sotera Wireless, Inc. Body-worn device and associated system for alarms/alerts based on vital signs and motion
US9492092B2 (en) 2009-05-20 2016-11-15 Sotera Wireless, Inc. Method for continuously monitoring a patient using a body-worn device and associated system for alarms/alerts
US8672854B2 (en) 2009-05-20 2014-03-18 Sotera Wireless, Inc. System for calibrating a PTT-based blood pressure measurement using arm height
US20100324387A1 (en) * 2009-06-17 2010-12-23 Jim Moon Body-worn pulse oximeter
US20100324388A1 (en) * 2009-06-17 2010-12-23 Jim Moon Body-worn pulse oximeter
US9596999B2 (en) 2009-06-17 2017-03-21 Sotera Wireless, Inc. Body-worn pulse oximeter
US8554297B2 (en) 2009-06-17 2013-10-08 Sotera Wireless, Inc. Body-worn pulse oximeter
US9775529B2 (en) 2009-06-17 2017-10-03 Sotera Wireless, Inc. Body-worn pulse oximeter
US8622922B2 (en) 2009-09-14 2014-01-07 Sotera Wireless, Inc. Body-worn monitor for measuring respiration rate
US8740807B2 (en) 2009-09-14 2014-06-03 Sotera Wireless, Inc. Body-worn monitor for measuring respiration rate
US20110066081A1 (en) * 2009-09-14 2011-03-17 Hiroshi Goto Sensor-Based Health Monitoring System
US8172777B2 (en) * 2009-09-14 2012-05-08 Empire Technology Development Llc Sensor-based health monitoring system
US8545417B2 (en) 2009-09-14 2013-10-01 Sotera Wireless, Inc. Body-worn monitor for measuring respiration rate
US8527038B2 (en) 2009-09-15 2013-09-03 Sotera Wireless, Inc. Body-worn vital sign monitor
US20110066042A1 (en) * 2009-09-15 2011-03-17 Texas Instruments Incorporated Estimation of blood flow and hemodynamic parameters from a single chest-worn sensor, and other circuits, devices and processes
US20110098583A1 (en) * 2009-09-15 2011-04-28 Texas Instruments Incorporated Heart monitors and processes with accelerometer motion artifact cancellation, and other electronic systems
US20110066041A1 (en) * 2009-09-15 2011-03-17 Texas Instruments Incorporated Motion/activity, heart-rate and respiration from a single chest-worn sensor, circuits, devices, processes and systems
WO2011039745A1 (en) * 2009-09-30 2011-04-07 Healthwatch Ltd. Continuous non-interfering health monitoring and alert system
CN102665535A (en) * 2009-09-30 2012-09-12 健康监测有限公司 Continuous non-interfering health monitoring and alert system
US9153112B1 (en) 2009-12-21 2015-10-06 Masimo Corporation Modular patient monitor
US9847002B2 (en) 2009-12-21 2017-12-19 Masimo Corporation Modular patient monitor
US20110148641A1 (en) * 2009-12-21 2011-06-23 Electronics And Telecommunications Research Institute Apparatus for detecting survival status of living thing and method using the same
US8565109B1 (en) 2010-01-29 2013-10-22 University Of Washington Through Its Center Of Commercialization Optimization of polling protocols in sensor networks
US9078259B2 (en) 2010-01-29 2015-07-07 University Of Washington Through Its Center For Commercialization Optimization of polling protocols in sensor networks
US8727977B2 (en) 2010-03-10 2014-05-20 Sotera Wireless, Inc. Body-worn vital sign monitor
US8591411B2 (en) 2010-03-10 2013-11-26 Sotera Wireless, Inc. Body-worn vital sign monitor
US8747330B2 (en) 2010-04-19 2014-06-10 Sotera Wireless, Inc. Body-worn monitor for measuring respiratory rate
US9173593B2 (en) 2010-04-19 2015-11-03 Sotera Wireless, Inc. Body-worn monitor for measuring respiratory rate
US9173594B2 (en) 2010-04-19 2015-11-03 Sotera Wireless, Inc. Body-worn monitor for measuring respiratory rate
US8979765B2 (en) 2010-04-19 2015-03-17 Sotera Wireless, Inc. Body-worn monitor for measuring respiratory rate
US8888700B2 (en) 2010-04-19 2014-11-18 Sotera Wireless, Inc. Body-worn monitor for measuring respiratory rate
US9339209B2 (en) 2010-04-19 2016-05-17 Sotera Wireless, Inc. Body-worn monitor for measuring respiratory rate
US9923644B2 (en) 2010-04-23 2018-03-20 Roche Diabetes Care, Inc. Method for generating a medical network
CN102859910A (en) * 2010-04-23 2013-01-02 霍夫曼-拉罗奇有限公司 Method for generating a medical network
WO2011131723A1 (en) 2010-04-23 2011-10-27 Roche Diagnostics Gmbh Method for generating a medical network
US20110288445A1 (en) * 2010-05-18 2011-11-24 Erik Lillydahl Systems and methods for reducing subconscious neuromuscular tension including bruxism
US8690800B2 (en) * 2010-05-18 2014-04-08 Erik Lillydahl Systems and methods for reducing subconscious neuromuscular tension including bruxism
US8814792B2 (en) 2010-07-27 2014-08-26 Carefusion 303, Inc. System and method for storing and forwarding data from a vital-signs monitor
US9585620B2 (en) 2010-07-27 2017-03-07 Carefusion 303, Inc. Vital-signs patch having a flexible attachment to electrodes
US9055925B2 (en) 2010-07-27 2015-06-16 Carefusion 303, Inc. System and method for reducing false alarms associated with vital-signs monitoring
US9615792B2 (en) 2010-07-27 2017-04-11 Carefusion 303, Inc. System and method for conserving battery power in a patient monitoring system
US9420952B2 (en) 2010-07-27 2016-08-23 Carefusion 303, Inc. Temperature probe suitable for axillary reading
US9357929B2 (en) 2010-07-27 2016-06-07 Carefusion 303, Inc. System and method for monitoring body temperature of a person
US9017255B2 (en) 2010-07-27 2015-04-28 Carefusion 303, Inc. System and method for saving battery power in a patient monitoring system
US9028404B2 (en) * 2010-07-28 2015-05-12 Foster-Miller, Inc. Physiological status monitoring system
US20120029299A1 (en) * 2010-07-28 2012-02-02 Deremer Matthew J Physiological status monitoring system
US20120029375A1 (en) * 2010-08-02 2012-02-02 Welch Allyn, Inc. Respirations Activity and Motion Measurement Using Accelerometers
US9596991B2 (en) * 2010-09-09 2017-03-21 Lg Electronics Inc. Self-examination apparatus and method for self-examination
US20120065476A1 (en) * 2010-09-09 2012-03-15 Kyuhyoung Choi Self-examination apparatus and method for self-examination
US8585606B2 (en) 2010-09-23 2013-11-19 QinetiQ North America, Inc. Physiological status monitoring system
US20130190577A1 (en) * 2010-10-07 2013-07-25 Swisstom Ag Sensor device for electrical impedance tomography imaging, electrical impedance tomography imaging instrument and electrical impedance tomography method
US9872087B2 (en) 2010-10-19 2018-01-16 Welch Allyn, Inc. Platform for patient monitoring
US9380952B2 (en) 2010-12-28 2016-07-05 Sotera Wireless, Inc. Body-worn system for continuous, noninvasive measurement of cardiac output, stroke volume, cardiac power, and blood pressure
US9585577B2 (en) 2010-12-28 2017-03-07 Sotera Wireless, Inc. Body-worn system for continuous, noninvasive measurement of cardiac output, stroke volume, cardiac power, and blood pressure
US9364158B2 (en) 2010-12-28 2016-06-14 Sotera Wirless, Inc. Body-worn system for continuous, noninvasive measurement of cardiac output, stroke volume, cardiac power, and blood pressure
US9439574B2 (en) 2011-02-18 2016-09-13 Sotera Wireless, Inc. Modular wrist-worn processor for patient monitoring
US8821380B2 (en) * 2011-05-30 2014-09-02 Olympus Medical Systems Corp. Antenna apparatus, antenna, antenna holder, and body-insertable apparatus system
US20130178702A1 (en) * 2011-05-30 2013-07-11 Olympus Medical Systems Corp. Antenna apparatus, antenna, antenna holder, and body-insertable apparatus system
US20120313746A1 (en) * 2011-06-10 2012-12-13 Aliphcom Device control using sensory input
US9069380B2 (en) 2011-06-10 2015-06-30 Aliphcom Media device, application, and content management using sensory input
WO2012170110A1 (en) * 2011-06-10 2012-12-13 Aliphcom Wearable device and platform for sensory input
WO2012171033A1 (en) * 2011-06-10 2012-12-13 Aliphcom Spacial and temporal vector analysis in wearable devices using sensor data
US20140266780A1 (en) * 2011-06-10 2014-09-18 Aliphcom Motion profile templates and movement languages for wearable devices
US8446275B2 (en) 2011-06-10 2013-05-21 Aliphcom General health and wellness management method and apparatus for a wellness application using data from a data-capable band
US9258670B2 (en) 2011-06-10 2016-02-09 Aliphcom Wireless enabled cap for a data-capable device
US9615547B2 (en) * 2011-07-14 2017-04-11 Petpace Ltd. Pet animal collar for health and vital signs monitoring, alert and diagnosis
US9913617B2 (en) 2011-10-13 2018-03-13 Masimo Corporation Medical monitoring hub
US9943269B2 (en) 2011-10-13 2018-04-17 Masimo Corporation System for displaying medical monitoring data
US9436645B2 (en) 2011-10-13 2016-09-06 Masimo Corporation Medical monitoring hub
US9700223B2 (en) 2011-12-02 2017-07-11 Lumiradx Uk Ltd Method for forming a component of a wearable monitor
US9700222B2 (en) 2011-12-02 2017-07-11 Lumiradx Uk Ltd Health-monitor patch
US9854986B2 (en) 2011-12-02 2018-01-02 Lumiradx Uk Ltd Health-monitor patch
US9734304B2 (en) 2011-12-02 2017-08-15 Lumiradx Uk Ltd Versatile sensors with data fusion functionality
USD788312S1 (en) 2012-02-09 2017-05-30 Masimo Corporation Wireless patient monitoring device
US20130310656A1 (en) * 2012-05-21 2013-11-21 Gukchan LIM Mobile terminal with health care function and method of controlling the mobile terminal
US20140088994A1 (en) * 2012-09-21 2014-03-27 CardioMEMS, Inc Method and system for trend-based patient management
US20140094677A1 (en) * 2012-10-02 2014-04-03 Seiko Instruments Inc. Biological information detecting apparatus and fixing structure
US9138158B2 (en) * 2012-10-02 2015-09-22 Seiko Instruments Inc. Biological information detecting apparatus and fixing structure
US9526437B2 (en) 2012-11-21 2016-12-27 i4c Innovations Inc. Animal health and wellness monitoring using UWB radar
US9865176B2 (en) 2012-12-07 2018-01-09 Koninklijke Philips N.V. Health monitoring system
US9706962B1 (en) * 2012-12-19 2017-07-18 Alert Core, Inc. Apparatus and method for teaching and algorithms for identifying qualifying movements
US9224311B2 (en) 2013-09-17 2015-12-29 Sony Corporation Combining data sources to provide accurate effort monitoring
US8795138B1 (en) 2013-09-17 2014-08-05 Sony Corporation Combining data sources to provide accurate effort monitoring
US9142141B2 (en) 2013-09-17 2015-09-22 Sony Corporation Determining exercise routes based on device determined information
US9717432B2 (en) 2013-09-25 2017-08-01 Bardy Diagnostics, Inc. Extended wear electrocardiography patch using interlaced wire electrodes
US9655538B2 (en) 2013-09-25 2017-05-23 Bardy Diagnostics, Inc. Self-authenticating electrocardiography monitoring circuit
US9655537B2 (en) 2013-09-25 2017-05-23 Bardy Diagnostics, Inc. Wearable electrocardiography and physiology monitoring ensemble
US9642537B2 (en) * 2013-09-25 2017-05-09 Bardy Diagnostics, Inc. Ambulatory extended-wear electrocardiography and syncope sensor monitor
US9700227B2 (en) 2013-09-25 2017-07-11 Bardy Diagnostics, Inc. Ambulatory electrocardiography monitoring patch optimized for capturing low amplitude cardiac action potential propagation
US9615763B2 (en) 2013-09-25 2017-04-11 Bardy Diagnostics, Inc. Ambulatory electrocardiography monitor recorder optimized for capturing low amplitude cardiac action potential propagation
US9619660B1 (en) 2013-09-25 2017-04-11 Bardy Diagnostics, Inc. Computer-implemented system for secure physiological data collection and processing
US9717433B2 (en) 2013-09-25 2017-08-01 Bardy Diagnostics, Inc. Ambulatory electrocardiography monitoring patch optimized for capturing low amplitude cardiac action potential propagation
US9730641B2 (en) 2013-09-25 2017-08-15 Bardy Diagnostics, Inc. Monitor recorder-implemented method for electrocardiography value encoding and compression
US9955888B2 (en) 2013-09-25 2018-05-01 Bardy Diagnostics, Inc. Ambulatory electrocardiography monitor recorder optimized for internal signal processing
US9730593B2 (en) 2013-09-25 2017-08-15 Bardy Diagnostics, Inc. Extended wear ambulatory electrocardiography and physiological sensor monitor
US9737211B2 (en) 2013-09-25 2017-08-22 Bardy Diagnostics, Inc. Ambulatory rescalable encoding monitor recorder
US9737224B2 (en) 2013-09-25 2017-08-22 Bardy Diagnostics, Inc. Event alerting through actigraphy embedded within electrocardiographic data
US9433380B1 (en) 2013-09-25 2016-09-06 Bardy Diagnostics, Inc. Extended wear electrocardiography patch
US9545204B2 (en) 2013-09-25 2017-01-17 Bardy Diagnostics, Inc. Extended wear electrocardiography patch
US9775536B2 (en) 2013-09-25 2017-10-03 Bardy Diagnostics, Inc. Method for constructing a stress-pliant physiological electrode assembly
US9433367B2 (en) 2013-09-25 2016-09-06 Bardy Diagnostics, Inc. Remote interfacing of extended wear electrocardiography and physiological sensor monitor
US9901274B2 (en) 2013-09-25 2018-02-27 Bardy Diagnostics, Inc. Electrocardiography patch
US9408545B2 (en) 2013-09-25 2016-08-09 Bardy Diagnostics, Inc. Method for efficiently encoding and compressing ECG data optimized for use in an ambulatory ECG monitor
US9955911B2 (en) 2013-09-25 2018-05-01 Bardy Diagnostics, Inc. Electrocardiography and respiratory monitor recorder
US9545228B2 (en) 2013-09-25 2017-01-17 Bardy Diagnostics, Inc. Extended wear electrocardiography and respiration-monitoring patch
US9820665B2 (en) 2013-09-25 2017-11-21 Bardy Diagnostics, Inc. Remote interfacing of extended wear electrocardiography and physiological sensor monitor
US9955885B2 (en) 2013-09-25 2018-05-01 Bardy Diagnostics, Inc. System and method for physiological data processing and delivery
US9554715B2 (en) 2013-09-25 2017-01-31 Bardy Diagnostics, Inc. System and method for electrocardiographic data signal gain determination with the aid of a digital computer
US9408551B2 (en) 2013-11-14 2016-08-09 Bardy Diagnostics, Inc. System and method for facilitating diagnosis of cardiac rhythm disorders with the aid of a digital computer
WO2015080701A1 (en) * 2013-11-26 2015-06-04 Rivas Alvarez Victor Telemetric health monitoring devices and system
EP3094173A4 (en) * 2014-01-16 2017-03-22 Petpace Ltd Pet animal collar for health&vital signs monitoring, alert and diagnosis
WO2015107521A1 (en) 2014-01-16 2015-07-23 Petpace Ltd Pet animal collar for health & vital signs monitoring, alert and diagnosis
US9269119B2 (en) 2014-01-22 2016-02-23 Sony Corporation Devices and methods for health tracking and providing information for improving health
US20170010658A1 (en) * 2014-02-24 2017-01-12 Sony Corporation Smart wearable devices and methods with power consumption and network load optimization
US20150366518A1 (en) * 2014-06-23 2015-12-24 Robert Sampson Apparatuses, Methods, Processes, and Systems Related to Significant Detrimental Changes in Health Parameters and Activating Lifesaving Measures
US20160029890A1 (en) * 2014-07-29 2016-02-04 Kurt Stump Computer-implemented systems and methods of automated physiological monitoring, prognosis, and triage
US9883801B2 (en) * 2014-07-29 2018-02-06 Kurt Stump Computer-implemented systems and methods of automated physiological monitoring, prognosis, and triage
US20160051155A1 (en) * 2014-08-19 2016-02-25 Kuo-Yuan Chang Patient vital signs monitoring system and vital signs monitor
US20160058379A1 (en) * 2014-08-26 2016-03-03 PetPlace Ltd. Animal of Equidae Family Band or Collar for Health & Vital Signs Monitoring, Alert and Diagnosis
US9504423B1 (en) 2015-10-05 2016-11-29 Bardy Diagnostics, Inc. Method for addressing medical conditions through a wearable health monitor with the aid of a digital computer
US9936875B2 (en) 2015-10-05 2018-04-10 Bardy Diagnostics, Inc. Health monitoring apparatus for initiating a treatment of a patient with the aid of a digital computer
US9788722B2 (en) 2015-10-05 2017-10-17 Bardy Diagnostics, Inc. Method for addressing medical conditions through a wearable health monitor with the aid of a digital computer
WO2017218907A1 (en) * 2016-06-16 2017-12-21 Arizona Board Of Regents On Behalf Of The University Of Arizona Systems, devices, and methods for determining an overall strength envelope

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