WO2016151479A1 - Gestion intelligente d'alimentation de capteurs pour article vestimentaire de santé - Google Patents

Gestion intelligente d'alimentation de capteurs pour article vestimentaire de santé Download PDF

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Publication number
WO2016151479A1
WO2016151479A1 PCT/IB2016/051595 IB2016051595W WO2016151479A1 WO 2016151479 A1 WO2016151479 A1 WO 2016151479A1 IB 2016051595 W IB2016051595 W IB 2016051595W WO 2016151479 A1 WO2016151479 A1 WO 2016151479A1
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Prior art keywords
sensor
wearable device
wearable
threshold
sensor data
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PCT/IB2016/051595
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English (en)
Inventor
John Cronin
Steven Philbin
Seth Cronin
Original Assignee
Koninklijke Philips N.V.
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Publication of WO2016151479A1 publication Critical patent/WO2016151479A1/fr

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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
    • G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
    • G16H40/63—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for local operation
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/0002—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network
    • A61B5/0015—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network characterised by features of the telemetry system
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
    • G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
    • G16H40/67—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/01—Measuring temperature of body parts ; Diagnostic temperature sensing, e.g. for malignant or inflamed tissue
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, 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
    • A61B5/021—Measuring pressure in heart or blood vessels
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, 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
    • A61B5/024—Detecting, measuring or recording pulse rate or heart rate
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/08—Detecting, measuring or recording devices for evaluating the respiratory organs
    • A61B5/0816—Measuring devices for examining respiratory frequency
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue
    • A61B5/14532—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue for measuring glucose, e.g. by tissue impedance measurement
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue
    • A61B5/14542—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue for measuring blood gases
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
    • A61B5/6801—Arrangements 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
    • A61B5/6802—Sensor mounted on worn items
    • A61B5/6803—Head-worn items, e.g. helmets, masks, headphones or goggles
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
    • A61B5/6801—Arrangements 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
    • A61B5/6802—Sensor mounted on worn items
    • A61B5/6804—Garments; Clothes
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
    • A61B5/6801—Arrangements 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
    • A61B5/6813—Specially adapted to be attached to a specific body part
    • A61B5/6824—Arm or wrist
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
    • A61B5/6801—Arrangements 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
    • A61B5/683—Means for maintaining contact with the body
    • A61B5/6831—Straps, bands or harnesses

Definitions

  • the present disclosure generally relates to wearable technology. More specifically, the present disclosure relates to wearable devices with power management technologies.
  • Wearable technology may include any type of mobile electronic device that can be worn on the body, that is attached to or embedded in clothes, and various other accessories of an individual.
  • This wearable technology currently exists in the consumer marketplace.
  • Processors and sensors associated with the wearable technology can display, process, and/or gather information.
  • Such wearable technology has been used in a variety of areas, including monitoring health data of the user as well as other types of data and statistics. These types of devices may be readily available to the public and may be easily purchased by consumers. Examples of some wearable technology in the health arena include the FitBit ® , the Nike+ FuelBand ® , the Up by Jawbone ® , and the Apple Watch ® .
  • a wearable device may include, for example, a number of sensors that can be used to gather health/fitness data about the user of the wearable device (e.g., pedometer, heart rate monitor) or the environment around the user (e.g. thermometer, humidity sensor) of the wearable device.
  • sensors e.g., pedometer, heart rate monitor
  • the environment around the user e.g. thermometer, humidity sensor
  • Each sensor or other component of a wearable device generally draws power from a battery.
  • a wearable device may have some understanding of how much battery power a particular component generally uses.
  • Wearable devices are typically small devices, which in turn means that their batteries are small, which in turn means that they must be charged often.
  • wearable devices must have their batteries recharged after less than 24 hours of use. Thus, battery life is often a significant barrier to wearable use.
  • Some wearable devices include settings allowing a user to adjust or reduce power consumption of the wearable device, generally by turning off a sensor, display, or other component. Using this approach, however, generally means that the user of the wearable device is no longer able to enjoy the functionality provided by the turned off component.
  • Other approaches such as turning off the display when a sensor detects that the user is not looking at the display, require frequent expenditure of energy by the sensor(s) used to detect if the user is looking at the display, and aren't applicable to much else besides the display (e.g. ⁇ other sensors).
  • the present disclosure is directed to inventive systems and methods for a wearable device that can automatically modify a sensor sampling rate based on a relationship of the sensor's data to a threshold.
  • An exemplary method may include a wearable device receiving a first physiology sensor input including one or more physiology sensor measurements from one or more physiology sensors of a wearable device, the first physiology sensor input being the latest of a first plurality of physiology sensor inputs received according to a base interval.
  • the wearable device may then determine that the first physiology sensor input falls outside of a predetermined health range.
  • the wearable device may then generate an alert at the wearable device.
  • the wearable device may then receive a second physiology sensor input, the second physiology sensor input being the first of a second plurality of physiology sensor inputs received according to a threshold interval, wherein the threshold interval is faster than the base interval.
  • a method for modifying the sampling frequency of a sensor of a wearable device includes the steps of: (i) providing a wearable device, the wearable device comprising a sensor and a processor; (ii) receiving, at a first frequency, sensor data from the sensor; (iii) comparing, by the processor, the received sensor data to a predetermined sensor data threshold; and (iv) automatically adjusting, if the received sensor data exceeds the predetermined sensor data threshold, the first frequency to a second frequency, wherein the second frequency is greater than the first frequency.
  • the method includes the steps of: receiving, at the second frequency, sensor data from the sensor; comparing, by the processor, the received sensor data to the predetermined sensor data threshold; and automatically adjusting, if the received sensor data is below the predetermined sensor data threshold, the second frequency to the first frequency.
  • the method further includes the step of receiving the predetermined sensor data threshold.
  • the method further includes the step of storing the received predetermined sensor data threshold in a memory of the wearable device.
  • the wearable device further includes a user interface configured to receive the predetermined sensor data threshold.
  • the method further includes the step of estimating, based on the first frequency and/or the second frequency, a remaining charge time of a battery of the wearable device.
  • the wearable device includes a sensor configured to obtain sensor data, a memory configured to store a predetermined sensor data threshold, and a processor in communication with the sensor and the memory, the processor being configured to: receive, at a first frequency, sensor data from the sensor; compare the received sensor data to a predetermined sensor data threshold; and automatically adjust, if the received sensor data exceeds the predetermined sensor data threshold, the first frequency to a second frequency, where the second frequency is greater than the first frequency.
  • the processor is further configured to receive, at the second frequency, sensor data from the sensor; compare the received sensor data to the predetermined sensor data threshold; and automatically adjust, if the received sensor data is below the predetermined sensor data threshold, the second frequency to the first frequency.
  • the processor is further configured to receive the predetermined sensor data threshold.
  • the device further includes a user interface configured to receive the predetermined sensor data threshold.
  • the processor is further configured to estimate, based on the first frequency and/or the second frequency, a remaining charge time of a battery (20) of the wearable device.
  • the device further includes a display configured to display at least one of the predetermined sensor data threshold, the sensor data, the first frequency, and the second frequency.
  • a display configured to display at least one of the predetermined sensor data threshold, the sensor data, the first frequency, and the second frequency.
  • the system includes: (i) a wearable device comprising: a sensor configured to obtain sensor data; a processor in communication with the sensor and the memory, where the processor is configured to: receive, at a first frequency, sensor data from the sensor; compare the received sensor data to a predetermined sensor data threshold; and automatically adjust, if the received sensor data exceeds the predetermined sensor data threshold, the first frequency to a second frequency, wherein the second frequency is greater than the first frequency; and (ii) a network device in communication with the wearable device and comprising a memory configured to store the predetermined sensor data.
  • the wearable device further comprises a user interface configured to receive the predetermined sensor data threshold, and the processor is further configured to send the received sensor data threshold to the network.
  • controller is used generally to describe various apparatus relating to the operation of a ventilator apparatus, system, or method.
  • a controller can be implemented in numerous ways (e.g., such as with dedicated hardware) to perform various functions discussed herein.
  • processor is one example of a controller which employs one or more microprocessors that may be programmed using software (e.g., microcode) to perform various functions discussed herein.
  • a controller may be implemented with or without employing a processor, and also may be implemented as a combination of dedicated hardware to perform some functions and a processor (e.g., one or more programmed microprocessors and associated circuitry) to perform other functions.
  • controller components that may be employed in various embodiments of the present disclosure include, but are not limited to, conventional microprocessors, application specific integrated circuits (ASICs), and field-programmable gate arrays (FPGAs).
  • a processor or controller may be associated with one or more storage media (generically referred to herein as "memory,” e.g., volatile and non-volatile computer memory such as RAM, PROM, EPROM, and EEPROM, floppy disks, compact disks, optical disks, magnetic tape, etc.).
  • the storage media may be encoded with one or more programs that, when executed on one or more processors and/or controllers, perform at least some of the functions discussed herein.
  • Various storage media may be fixed within a processor or controller or may be transportable, such that the one or more programs stored thereon can be loaded into a processor or controller so as to implement various aspects of the present invention discussed herein.
  • program or “computer program” are used herein in a generic sense to refer to any type of computer code (e.g., software or microcode) that can be employed to program one or more processors or controllers.
  • user interface refers to an interface between a human user or operator and one or more devices that enables communication between the user and the device(s).
  • user interfaces that may be employed in various implementations of the present disclosure include, but are not limited to, switches, potentiometers, buttons, dials, sliders, track balls, display screens, various types of graphical user interfaces (GUIs), touch screens, microphones and other types of sensors that may receive some form of human-generated stimulus and generate a signal in response thereto.
  • GUIs graphical user interfaces
  • Various embodiments of the present invention may further include non- transitory computer-readable storage media, having embodied thereon a firewall program executable by a processor to perform methods described herein.
  • FIG. 1 is a schematic representation of a wearable device system including a wearable device connected to a network through a communications network, in accordance with an embodiment.
  • FIG. 2A illustrates a network with exemplary software associated with power management functions, in accordance with an embodiment.
  • FIG. 2B illustrates a wearable device with exemplary software associated with power management functions, in accordance with an embodiment.
  • FIG. 3 A illustrates a sensor selection graphical user interface (GUI) as displayed by a wearable device, in accordance with an embodiment.
  • GUI sensor selection graphical user interface
  • FIG. 3B illustrates a sensor settings graphical user interface (GUI) as displayed by a wearable device, in accordance with an embodiment.
  • GUI graphical user interface
  • FIG. 3C illustrates a user alert graphical user interface (GUI) as displayed by a wearable device, in accordance with an embodiment.
  • GUI graphical user interface
  • FIG. 4A illustrates a preset choices graphical user interface (GUI) as displayed by a wearable device, in accordance with an embodiment.
  • GUI graphical user interface
  • FIG. 4B illustrates a preset sensor selection graphical user interface (GUI) as displayed by a wearable device, in accordance with an embodiment.
  • FIG. 4C illustrates a preset sensor setting graphical user interface (GUI) as displayed by a wearable device, in accordance with an embodiment.
  • FIG. 5 illustrates a list of exemplary network threshold level databases as stored by a network, in accordance with an embodiment.
  • FIG. 6 is a schematic representation of a computing device architecture, in accordance with an embodiment.
  • FIG. 7 A illustrates a wearable threshold level database as stored by a wearable device, in accordance with an embodiment.
  • FIG. 7B illustrates a network download database as stored by a network, in accordance with an embodiment.
  • FIG. 8A illustrates a wearable sensor database as stored by a wearable device, in accordance with an embodiment.
  • FIG. 8B illustrates a wearable threshold sensor data database as stored by a wearable device, in accordance with an embodiment.
  • FIG. 9 is a flow diagram illustrating operations of a wearable sensor settings software of a wearable device, in accordance with an embodiment.
  • FIG. 10 is a flow diagram illustrating operations of a wearable download software of a wearable device interacting with a network download software of a network, in accordance with an embodiment.
  • FIG. 11A is a flow diagram illustrating operation of a wearable monitoring software of a wearable device, in accordance with an embodiment.
  • FIG. 11B is a flow diagram illustrating exemplary operation of a wearable action software of a wearable device, in accordance with an embodiment.
  • FIG. 12 is a flow diagram illustrating operation of a wearable threshold monitoring software of a wearable device, in accordance with an embodiment.
  • FIG. 13 is a flowchart of a method for modifying a sensor sampling rate based on a relationship of the sensor's data to a threshold, in accordance with an embodiment.
  • FIG. 14 is a flowchart of a method for modifying a sensor sampling rate based on a relationship of the sensor's data to a threshold, in accordance with an embodiment.
  • Embodiments disclosed herein include a system and method that adjusts the sampling rate of at least one sensor according to contextual requirements, in order to preserve battery life.
  • Embodiments relate to, for example, a wearable device which may include one or more sensors, which the wearable device may use to periodically take sensor measurements of physiological parameters (e.g. 3 heart rate) of a user of the wearable device according to a base measurement rate (e.g., every 10 minutes). Each sensor measurement is compared to a pre-determined threshold level (e.g. ⁇ predetermined at the wearable device or at a network).
  • a pre-determined threshold level e.g. ⁇ predetermined at the wearable device or at a network.
  • the wearable device If a measurement exceeds the predetermined threshold level, the wearable device generates an alert at the wearable device and begins to periodically take sensor measurements at a faster threshold measurement rate (e.g., every 10 seconds) using the sensor whose measurement exceeded the threshold limit. If a new measurement falls below the threshold limit, the wearable device can return to using the base measurement rate. Alternatively, if a measurement falls below a pre-determined threshold level, the wearable device generates an alert at the wearable device and begins to periodically take sensor measurements at a lower threshold measurement rate (e.g., every 15 minutes) using the sensor whose measurement is below the threshold limit. If a new measurement rises above the threshold limit, the wearable device can return to using the base measurement rate. Thus, the wearable device's sensor-used battery power is conserved unless a sensor measurement indicates that they are needed due to a potential health risk.
  • a faster threshold measurement rate e.g., every 10 seconds
  • the wearable device can return to using the base measurement rate.
  • FIG. 1 illustrates an exemplary wearable device ecosystem 100 including an exemplary wearable device 10 connected to an exemplary network 30 through an internet/cloud connection 40.
  • wearable device 10 may include various components.
  • the wearable device may include a display 14, a clock 16, a communication module 18, one or more sensors 12, a power storage unit 20, a vibrator 22, a processor 52, and a memory 24, among many other possible components. These components may be communicatively coupled at a single bus, or may alternatively be connected in a more disjointed manner.
  • the memory 24 of the wearable device may include, among other things, an operating system, a wearable monitoring software (see e.g., FIG. 11 A), a wearable threshold monitoring software (see e.g., FIG. 12), a wearable action software (see e.g.,
  • FIG. 11B a user alert graphical user interface (GUI) (see e.g., FIG. 3C) that is associated with the wearable action software, a wearable sensor settings software (see e.g., FIG. 9), a user sensor selection GUI (see e.g., FIG. 3A) that is associated with the wearable sensor setting software, a user sensor settings GUI (see e.g., FIG. 3B) that is associated with the wearable sensor setting software, a wearable download software (see e.g., FIG. 10), a preset choices GUI (see e.g., FIG. 4A) that is associated with the wearable download software, a preset sensor selection GUI (see e.g., FIG.
  • FIG. 4B that is associated with the wearable download software
  • a preset sensor settings GUI that is associated with the wearable download software
  • a wearable threshold level database see e.g., FIG. 7A
  • a wearable sensor database see e.g., FIG. 8A
  • a wearable threshold sensor data database see e.g., FIG. 8B. See FIG. 2A for another possible organization of the software elements in the memory of the wearable device.
  • the wearable device architecture illustrated in FIG. 1 should be interpreted as illustrative rather than limiting, and other embodiments may include additional or different components and/or elements stored in memory, and/or may lack illustrated components or elements stored in memory.
  • the software stored by the wearable device may, when executed, allow the user of the wearable device to set/select specific threshold levels and reading rates corresponding to each sensor of the one or more sensors 12 of the user's wearable device 10.
  • the wearable download software which includes the preset choices GUI, the preset sensor selection GUI, and the preset sensor setting GUI, may allow the user of the wearable device to download preset sensor settings for to each sensor of the one or more sensors of the user's wearable device. Presets could be based on user variables such as fitness level (e.g., athlete versus couch potato), health issues (e.g., high blood pressure or heart attack risk), genetic predispositions (e.g., family history of heart attack risk), age, or gender, among many others.
  • fitness level e.g., athlete versus couch potato
  • health issues e.g., high blood pressure or heart attack risk
  • genetic predispositions e.g., family history of heart attack risk
  • age or gender, among many others.
  • the databases stored by the wearable device may, when used by the software stored by the wearable device, store useful information for operating the sensors of the wearable device.
  • the wearable threshold level database 26 may store threshold levels for each sensor of the one or more sensors of the wearable device that, if the threshold levels are reached, will trigger a quicker reading rate.
  • the wearable sensor database 28 may be the location where the wearable monitoring software saves wearable sensor readings.
  • the wearable threshold sensor database 32 may the location where the wearable threshold monitoring software saves wearable sensor readings.
  • the communication module 18 of the wearable device may be a wired connection module (e.g., a USB port module, a FireWire port module, a Lightning port module, a Thunderbolt port module), a physical connection module such as one that communicates through a direct physical contact of one or more conductive leads of the wearable device to one or more conductive leads of another device, connector, or power source, a wireless connection module such as a Wi-Fi connection module, a 3G/4G/LTE cellular connection module, a Bluetooth connection module, a Bluetooth low energy connection module, Bluetooth Smart connection module, a near field communication module, a radio wave communications module, a magnetic induction power transmitter/receiver, or a magnetic resonance power transmitter/receiver, or the communication module may be some combination thereof.
  • communications network 40 may be a wired or wireless communication network.
  • the one or more wearable device 10 sensors of the wearable device 10 may include sensors for measuring blood pressure, heart rate, pulse, blood oxygen (e.g., a pulse oximeter), body temperature (e.g., a thermometer), blood sugar, blood glucose
  • a glucometer e.g., a glucometer
  • acceleration e.g., an accelerometer
  • insulin e.g., insulin, vitamin levels, respiratory rate, heart sound (e.g., a microphone), breathing sound (e.g., a microphone), movement speed (e.g., an accelerometer), movement acceleration (e.g., an accelerometer), steps walked or ran (e.g., a pedometer), skin moisture, sweat detection, sweat composition, nerve firings (e.g., an electromagnetic sensor), or similar health measurements.
  • additional sensors may also measure allergens, air quality, air humidity, air temperature, and other environmental measurements.
  • sensor 12 is in continuous or periodic communication with a processor 52.
  • processor 52 is a controller or processor in communication with the sensors 12 and one or more other components of the wearable device 10.
  • processor 52 may instruct sensor 12 to sample a certain frequency, or the sensor 12 may automatically sample data at a default frequency.
  • processor 52 may drive or contact or query sensor 12 for each sample (processor 52 thereby setting the sampling frequency according to the frequency that it instructs sensor 12 to sample).
  • processor 52 may instruct an intermediate component to query sensor 12 at a particular sampling frequency.
  • sampling frequency of the sensor 12 may be set and executed by any method as is known in the art for setting the sampling frequency of a sensor 12.
  • processor 52 may perform the calculations and comparisons described herein, although one of ordinary skill will appreciate that each sensor 12 may be equipped with the computational power necessary to perform the required calculation. Alternately, the calculations may be performed by a paired local device or by a remote server.
  • the power storage unit 20 may be any type of unit capable of storing power over a period of time, such as a rechargeable battery (e.g., Nickel Cadmium or "NiCd", Nickel Metal Hydride or “NiMH”, Lithium Ion or “Li Ion”, Sealed Lead Acid or “SLA”), a capacitor, a potential-energy-based power storage unit, a chemical-energy-based power storage unit, a kinetic-energy-based power storage unit, or some combination thereof.
  • a rechargeable battery e.g., Nickel Cadmium or "NiCd", Nickel Metal Hydride or "NiMH”, Lithium Ion or “Li Ion”, Sealed Lead Acid or “SLA”
  • a capacitor e.g., a potential-energy-based power storage unit, a chemical-energy-based power storage unit, a kinetic-energy-based power storage unit, or some combination thereof.
  • Battery e.g., Battery
  • the power storage unit may also include embedded sensors and/or processors.
  • some rechargeable batteries include sensors and processors that, working together, limit or otherwise control discharging and recharging of the rechargeable battery in ways that help preserve or increase the battery's lifespan, or that help prevent potentially dangerous conditions from forming while the rechargeable battery is discharging or recharging.
  • the display 14 may be a touch-sensitive display (e.g., a capacitive multi-touch display) to allow a user to interact with a graphical user interface displayed through the display.
  • the display can also be non-touch-sensitive, and any user interface described herein may be instead operated through physical/mechanical interface components such as buttons, radio buttons, levers, switches, wheels, sliders, touchpads, keyboards, mice, and other physical/mechanical interface elements embedded within or connected to the wearable device.
  • the display may be a liquid crystal display (LCD), gas plasma display, electronic ink display, light emitting diode (LED) display, Organic LED (OLED) display, field emission display (FED), surface-conduction electron-emitter display (SED), cathode ray tube (CRT) display, or any other type of light-reflective or light- transmissive display that can be used with a computing device.
  • LCD liquid crystal display
  • LED light emitting diode
  • OLED Organic LED
  • FED field emission display
  • SED surface-conduction electron-emitter display
  • CRT cathode ray tube
  • the wearable device may include other components that could reasonably fit into a wearable device, or be connected (in a wired or wireless fashion) to a wearable device.
  • the wearable device may include one or more speakers, one or more microphones, one or more lights (e.g., LED lights), one or more camera devices, or one or more thermal sensors, among many other possible components.
  • the memory 24 of the wearable device may be any type of memory, including a flash memory (NOR flash or NAND flash), an electrically erasable programmable readonly memory (EEPROM), read-only memory (ROM), solid-state memory, random access memory (RAM), dynamic random access memory (DRAM), a hard drive (HDD), an optical-disc-based memory, a floppy-disk-based memory, a memristor-based memory, or a magnetic-tape-based memory.
  • NOR flash or NAND flash an electrically erasable programmable readonly memory (EEPROM), read-only memory (ROM), solid-state memory, random access memory (RAM), dynamic random access memory (DRAM), a hard drive (HDD), an optical-disc-based memory, a floppy-disk-based memory, a memristor-based memory, or a magnetic-tape-based memory.
  • EEPROM electrically erasable programmable readonly memory
  • ROM read-only memory
  • solid-state memory random access
  • database or “databases” herein should be understood to include any data structure that can hold data about one or more entities, such as a database, a table, a list, a matrix, an array, an arraylist, a tree, a hash, a flat file, an image, a queue, a heap, a memory, a stack, a set of registers, or a similar data structure.
  • the wearable device 10 could be primarily intended to be worn around a user's wrist (e.g., a watch or bracelet), neck (e.g., a necklace or scarf), arm (e.g., an armband or elbow brace), hand (e.g, a glove), finger (e.g., a ring), head (e.g., a hat or helmet or headband or headlamp), leg (e.g., a knee brace or leg holster or pair of pants), torso (e.g., a shirt or sweater or jacket), chest (e.g., a heart monitor chest band/patch, a respiratory monitor chest band/patch), pelvic area (e.g. ⁇ an undergarment or a swimsuit or a jock strap), waist (e.g., a belt), foot (e.g., a shoe or sock or ankle brace), or another area of the user's body.
  • wrist e.g., a watch or bracelet
  • neck e.
  • the wearable device 10 can connect to the communications network 40, such as the cloud/internet, through the communication module 18 of the wearable device.
  • the wearable device cannot connect directly to the communications network, but must connect through a proxy device (not shown).
  • the wearable device could connect to a mobile device (e.g. ⁇ a smartphone, tablet device, or laptop computer) through a wired or local wireless communications interface (e.g., universal serial bus or Bluetooth) which may then communicate with the network through a cloud/internet connection (e.g., Ethernet connection, Wi-Fi connection, 3G cellular connection, 4G cellular connection, Long Term Evolution cellular connection, Edge cellular connection and act an intermediary for communications between the wearable device and the network.
  • a cloud/internet connection e.g., Ethernet connection, Wi-Fi connection, 3G cellular connection, 4G cellular connection, Long Term Evolution cellular connection, Edge cellular connection and act an intermediary for communications between the wearable device and the network.
  • the network 30 may include one or more communication modules that can allow one or more computer systems within the network to communicate with the wearable device through the internet/cloud.
  • the one or more communication modules of the network may be wired connection modules (e.g., a USB port module, a FireWire port module, a Lightning port module, a Thunderbolt port module), physical connection modules (e.g., that communicates through a direct physical contact of one or more conductive leads of the computer system of the network to one or more conductive leads of another device, connector, or power source), wireless connection modules (e.g., a Wi-Fi connection module, a 3G/4G/LTE cellular connection module, a Bluetooth connection module, a Bluetooth low energy connection module, Bluetooth Smart connection module, a near field communication module, a radio wave communications module, a magnetic induction power transmitter/receiver, or a magnetic resonance power transmitter/receiver), or some combination thereof.
  • wired connection modules e.g., a USB port module, a FireWire port module
  • the network 30 may also include a memory (based at a single system of the network or distributed throughout the network) that stores a variety of software elements (that may be executed by one or more processors within the network) and storage elements (e.g., databases).
  • the memory of the network may include a network download software 38 (see e.g., FIG. 10), a network download database 36 (see e.g., FIG. 7B), and a network threshold level database 34 (see e.g., FIG. 5), among others.
  • the databases stored by network 30 may be a different type of data structure than a traditional database.
  • database or “databases” herein should be understood to include any data structure that can hold data about one or more entities, such as a database, a table, a list, a matrix, an array, an arraylist, a tree, a hash, a flat file, an image, a queue, a heap, a memory, a stack, a set of registers, or a similar data structure.
  • the databases stored by the network may, when used by the software stored by the network, store useful information for interacting with the wearable device.
  • the network download database may include a user's preset settings choices from when the wearable device is executing the wearable download software.
  • the preset choices may be associated with settings stored within the network threshold level database of the network.
  • the network download software of the network may receive selections from the wearable device regarding a particular preset setting within the network download database, obtain more information about that particular preset setting from the network threshold level database of the network, and transmit that information to the wearable device through the internet/cloud.
  • the network may include one or more computer systems. These computer systems may be networked together into a local area network (LAN) or wireless local area network (WLAN), or they may be independent of each other. These computer systems may each be any type of computer system, such as a laptop computer, a desktop computer, a structured query language (SQL) server, a web front-end server, a central administration server, an index server, a database server, an application server, a gateway server, a broker server, an active directory server, a terminal server, a virtualization services server, a virtualized server, a file server, a print server, an email server, a security server, a connection server, a search server, a license server, a "blade” server, a virtual machine, a "thin” client, a Redundant Arrays of Independent Disks (RAID) array, a gaming console, a smart television, a home entertainment system, as a smartphone, a tablet, a second wearable device, a portable
  • FIG. 2A illustrates an exemplary network 30 with exemplary network download software 38.
  • the exemplary network may execute the network download software (see e.g., FIG. 10).
  • the network download software may work in conjunction with the wearable download software of the wearable device to allow the user of the wearable device to select, through the wearable device, a preset settings choice that impacts the threshold level of one or more sensors of the wearable device.
  • the threshold level is then stored at the wearable threshold level database of the wearable device, so the preset settings choice includes pre-selected reading rates and threshold levels for each of the one or more settings associated with the preset settings choice.
  • the threshold levels indicate a sensor measurement threshold that, when reached, should trigger an increase in reading rate associated with one or more sensors.
  • FIG. 2B illustrates an exemplary wearable device 10 with exemplary software associated with power management functions.
  • the exemplary wearable device may execute a wearable download software 42 (see e.g., FIG. 10), a preset choices GUI 42a (see e.g., FIG. 4A) that is associated with the wearable download software, a preset sensor selection GUI 42b (see e.g., FIG. 4B) that is associated with the wearable download software, and a preset sensor settings GUI 42c (see e.g., FIG. 4C) that is associated with the wearable download software.
  • a wearable download software 42 see e.g., FIG. 10
  • a preset choices GUI 42a see e.g., FIG. 4A
  • a preset sensor selection GUI 42b see e.g., FIG. 4B
  • a preset sensor settings GUI 42c see e.g., FIG. 4C
  • the wearable download software and its various associated GUI elements may allow a user of the wearable device to select a specific preset setting from the network to their wearable device.
  • the wearable download software may then transmit that selection to the network's network download software.
  • the network download software receives that selection, compares it with the network download database, finds a matching entry in the network download database and/or the network threshold level database, and then copies and transmits data from these database entries back to the wearable download software of the wearable device. If the data from the network download software includes network threshold level database information, the wearable download software stores this in the wearable threshold level database.
  • FIG. 2B illustrates that the exemplary wearable device may also execute a wearable sensor settings software 44 (see e.g., FIG. 9), a user sensor selection GUI 44a (see e.g., FIG. 3A) that is associated with the wearable sensor setting software, and a user sensor settings GUI 44b (see e.g., FIG. 3B) that is associated with the wearable sensor setting software.
  • the wearable sensor settings software and its various associated GUI elements may allow a user of the wearable device to set custom threshold levels and reading rates for each different sensors of the one or more sensors of the wearable device.
  • FIG. 2B illustrates that the exemplary wearable device may also execute a wearable monitoring software 46 (see e.g., FIG. 11A), which may take the wearable sensor and wearable clock readings and compare those readings with the wearable threshold level database to try to locate a matching entry. If the wearable monitoring software locates a matching entry, a wearable action software is executed.
  • FIG. 2B illustrates that the exemplary wearable device may also execute the wearable action software 50 (see e.g., FIG. 11B) and a user alert graphical user interface (GUI) 50a (see e.g., FIG. 3C) that is associated with the wearable action software.
  • GUI user alert graphical user interface
  • the wearable action software may execute an alert action for a matching entry that was found in the wearable threshold level database by the wearable monitoring software.
  • the alert action may be an alert message (e.g., a textual message, a graphical message, or a video-based message) displayed through the user alert GUI (e.g., through the display of the wearable device).
  • the alert action may also include alternate techniques for alerting the user of the wearable device, such as by playing audio through a speaker of the wearable device, lighting up a light (e.g.,, a white/colored LED light) on the wearable device, or by activating a vibrator to vibrate part of the wearable device.
  • the wearable device may then execute a wearable threshold monitoring software.
  • FIG. 2B illustrates that the exemplary wearable device may also execute the wearable threshold monitoring software 48 (see e.g., FIG. 12).
  • the wearable threshold monitoring software may be similar in function to the wearable monitoring software, in that it may take the wearable sensor and wearable clock readings and compare those readings with the wearable threshold level database to try to locate a matching entry. The difference is that the wearable threshold monitoring software may take these sensor measurement readings at the threshold reading rate, instead of the base reading rate. It then may compare these sensor measurement readings with the wearable threshold level database. If there is a matching entry in the wearable threshold level database, the wearable threshold monitoring software simply continues to execute, since sensor measurement readings are already being taken at the threshold reading rate. If no matching entry is found in the wearable threshold level database, then the wearable device executes the wearable monitoring software instead of the wearable threshold monitoring software, so that the wearable monitoring software may take sensor measurement readings at the base reading rate in order to help conserve battery power.
  • FIG. 3A illustrates an exemplary sensor selection graphical user interface (GUI) 44a as displayed by an exemplary wearable device 10.
  • the sensor selection GUI may show a selection of sensor-type buttons, which each correlate to a sensor (or a set of sensors) of the one or more sensors found on wearable device.
  • FIG. 3B the user sensor settings GUI
  • there is a "download preset settings" button and that could open the preset choices GUI (see FIG. 4A) or another part of the wearable download software.
  • the bottom-right-hand corner the exemplary sensor selection GUI features a close button.
  • FIG. 3B illustrates an exemplary sensor settings graphical user interface (GUI)
  • the sensor settings GUI details a base reading rate, threshold level, and threshold reading rate for a particular sensor.
  • the particular sensor is a pulse sensor.
  • the exemplary sensor settings GUI of FIG. 3B includes three pull-down boxes allowing a user to choose different values for a base reading rate (e.g., every 10 minutes), threshold level (e.g., over 110 beats per minute), and threshold reading rate (e.g., every
  • the pulse sensor of the wearable device will take a reading according to the base reading rate (e.g., every 10 minutes). If the user's pulse exceeds the threshold level (e.g., 110 beats per minute), the pulse sensor of the wearable device will take a reading according to the threshold reading rate (e.g., every 10 seconds).
  • the pulse sensor of the wearable device will continue to take readings according to the threshold reading rate (e.g., every 10 seconds) until the user's pulse falls below the threshold level (e.g., 110 beats per minute), at which point the wearable device will return to checking the user's pulse according to the base reading rate (e.g., every 10 minutes).
  • the threshold reading rate e.g., every 10 seconds
  • the threshold level e.g., 110 beats per minute
  • the exemplary sensor settings GUI of FIG. 3B also includes two battery life estimates.
  • the first battery life estimate shows an estimated battery life at the base reading rate (i.e., if the threshold level is never exceeded). In the exemplary sensor settings GUI of FIG. 3B, this first estimate is 72 hours.
  • the second battery life estimate shows an estimated battery life at the threshold reading rate (i.e., if the threshold level is continually exceeded). In the exemplary sensor settings GUI of FIG. 3B, this second estimate is only 6 hours, due to higher-frequency usage of the pulse sensor.
  • the exemplary sensor settings GUI of FIG. 3B also includes a save button in the bottom-left- hand corner allowing the user of the wearable device to save any changes made to these settings, and a close button in the bottom-right-hand corner.
  • FIG. 3C illustrates an exemplary user alert graphical user interface (GUI) 50a as displayed by an exemplary wearable device.
  • GUI graphical user interface
  • the user alert GUI may be the alert
  • the user alert GUI may include a message box that may display a message to the user (e.g., "Pulse has reached Threshold Level. Pulse readings are being taken at
  • the user alert GUI may include current sensor data and/or historical sensor data at the time that the threshold level was exceeded. For example, the exemplary user alert GUI of FIG. 3C indicates that the pulse value measured by the pulse sensor(s) is 119 beats per minute (bpm), which is higher than the threshold limit, which, according to the exemplary sensor settings GUI of FIG. 3B, is 110 bpm.
  • the user alert GUI may also include a graph view showing pulse over a predetermined time span. In the exemplary user alert GUI of FIG. 3C, this graph view is located to the right of the pulse data and indicates that the pulse value has increased over time, which is consistent with the pulse's recent exceeding of the threshold.
  • the bottom-right-hand corner of the exemplary user alert GUI of FIG. 3C includes a close button.
  • FIG. 4A illustrates an exemplary preset choices graphical user interface (GUI)
  • the exemplary preset choices GUI may pop up if the user selected "download preset settings" at the exemplary user sensor selection GUI of FIG. 3A.
  • the exemplary preset choices GUI of FIG. 4A may show the different preset setting choices that the user of the wearable device can select.
  • Each preset setting choice is associated with an entry in the wearable threshold level database.
  • the exemplary preset choices GUI of FIG. 4A lists "college athlete,” “heart disease,” “diabetic,” “elderly,” and “40's male” as preset categories. Accordingly, the preset settings can be fairly specific.
  • the scroll bar setup also implies that more preset settings may be available for selection by the user.
  • the wearable device transitions into a preset sensor selection GUI (e.g., exemplary preset sensor selection GUI of FIG. 4B).
  • FIG. 4A also includes a "close” button in the bottom-right-hand corner.
  • FIG. 4B illustrates an exemplary preset sensor selection graphical user interface
  • GUI displayed by an exemplary wearable device.
  • the user may reach the preset sensor selection GUI by selecting a preset settings choice from the preset choices GUI of
  • the exemplary preset sensor selection GUI of FIG. 4B indicates that the "40's Male” preset settings choice was made.
  • the exemplary preset sensor selection GUI of FIG. 4B includes four buttons corresponding to different sensors of the wearable device (e.g., "pulse,” “temperature,” “blood pressure,” and “respiration”). If the user of the wearable device selects any of these buttons, the wearable device should transition into the preset sensor settings GUI (e.g., the exemplary preset sensor settings
  • GUI of FIG. 4B includes a download button, which, if selected, may trigger a download of the selected preset settings choice (e.g., "40's Male") and all associated information and settings ⁇ e.g., from the network threshold level database of the network) to the wearable threshold level database of the wearable device.
  • the exemplary preset sensor selection GUI of FIG. 4B also includes a "close” button in the bottom-right-hand corner.
  • FIG. 4C illustrates an exemplary preset sensor setting graphical user interface (GUI) 42c as displayed by an exemplary wearable device.
  • GUI graphical user interface
  • the wearable device should transition into the preset sensor setting GUI.
  • the exemplary preset sensor setting GUI of FIG. 4C describes a pulse sensor, and would naturally follow if a user of the exemplary preset sensor selection GUI of FIG. 4B selected the "pulse" button.
  • the exemplary preset sensor setting GUI of FIG. 4C similarly to the exemplary user sensor settings GUI of FIG.
  • 3B includes values for a base reading level (e.g., every 10 minutes), a threshold level (e.g., over 105 bpm), and a threshold reading level (e.g., every 10 seconds).
  • a base reading level e.g., every 10 minutes
  • a threshold level e.g., over 105 bpm
  • a threshold reading level e.g., every 10 seconds.
  • the pulse sensor of the wearable device will continue to take readings according to the threshold reading rate (e.g., every 10 seconds) until the user's pulse falls below the threshold level (e.g., 105 beats per minute), at which point the wearable device will return to checking the user's pulse according to the base reading rate (e.g., every 10 minutes).
  • the threshold reading rate e.g., every 10 seconds
  • the wearable device will return to checking the user's pulse according to the base reading rate (e.g., every 10 minutes).
  • the exemplary preset sensor setting GUI of FIG. 4C also includes two battery life estimates.
  • the first battery life estimate shows an estimated battery life at the base reading rate (i.e., if the threshold level is never exceeded). In the exemplary preset sensor setting GUI of FIG. 4C, this first estimate is 72 hours.
  • the second battery life estimate shows an estimated battery life at the threshold reading rate (i.e., if the threshold level is continually exceeded). In the exemplary preset sensor setting GUI of FIG. 4C, this second estimate is only 6 hours, due to higher-frequency usage of the pulse sensor.
  • the exemplary preset sensor setting GUI of FIG. 4C also includes a "close" button in the bottom-right-hand corner.
  • FIG. 5 illustrates embodiments of network threshold level databases 34 as stored by an exemplary network.
  • the network threshold level databases depicted in FIG. 5 include two visible entries ⁇ i.e., two visible exemplary network threshold level databases) - the "40's Male” database and the "College Athlete” database.
  • Each entry is an exemplary network threshold level database which stores preset settings that can be selected by a user at the preset choices GUI (e.g., exemplary preset choices GUI of FIG. 4A).
  • a scroll bar interface is used to imply that other entries may exist in the threshold level databases of FIG. 5 besides the two entries illustrated in FIG. 5 ("40' s Male” and "College Athlete").
  • the "40' s Male" exemplary preset includes base reading rates, threshold levels, threshold reading rates, and threshold alert actions/messages for each of four sensors (pulse, temperature, blood pressure, and respiration).
  • the base reading rate for all four sensors is "every 10 minutes.”
  • the threshold level is different for each sensor (such as pulse: over 105 bpm; temperature: over 101 degrees Fahrenheit; blood pressure: over 140/80 mm Hg; respiration: over 23 respirations per minute, as examples).
  • the threshold reading rate is different for each sensor (such as pulse: every 10 seconds; temperature: every 1 minute; blood pressure: every 30 seconds; respiration: every 10 seconds, for example).
  • the "College Athlete" exemplary preset includes base reading rates, threshold levels, threshold reading rates, and threshold alert actions/messages for each of four sensors (pulse, temperature, blood pressure, and respiration). The base reading rate for all four sensors is "every 10 minutes.” The threshold level is different for each sensor
  • the threshold reading rate is different for each sensor (such as pulse: every 5 seconds; temperature: every 1 minute; blood pressure: every 30 seconds; respiration: every 5 seconds, for example).
  • Each includes a message to be displayed by the user alert GUI indicating that the relevant threshold level has been exceeded, and that the wearable device is transitioning to use the threshold reading rate for the relevant sensor.
  • FIG. 6 illustrates an embodiment of a computing device architecture that may be utilized to implement the various features and processes described herein.
  • the computing device architecture 600 could be implemented in the wearable device 10 and/or a server of the network.
  • Architecture 600 as illustrated in FIG. 6 includes memory interface 602, processors 604, and peripheral interface 606.
  • Memory interface 602, processors 604 and peripherals interface 606 can be separate components or can be integrated as a part of one or more integrated circuits.
  • the various components can be coupled by one or more communication buses or signal lines.
  • Processors 604 as illustrated in FIG. 6 is meant to be inclusive of data processors, image processors, central processing unit, or any variety of multi-core processing devices. Any variety of sensors, external devices, and external subsystems can be coupled to peripherals interface 606 to facilitate any number of functionalities within the architecture 600 of the exemplar mobile device. For example, motion sensor
  • light sensor 612 could be utilized to facilitate adjusting the brightness of touch surface 646.
  • Motion sensor 610 which could be exemplified in the context of an accelerometer or gyroscope, could be utilized to detect movement and orientation of the mobile device. Display objects or media could then be presented according to a detected orientation (e.g., portrait or landscape).
  • peripherals interface 606 Other sensors could be coupled to peripherals interface 606, such as a temperature sensor, a biometric sensor, or other sensing device to facilitate corresponding functionalities.
  • Location processor 615 e.g., a global positioning transceiver
  • An electronic magnetometer 616 such as an integrated circuit chip could in turn be connected to peripherals interface 606 to provide data related to the direction of true magnetic North whereby the mobile device could enjoy compass or directional functionality.
  • Camera subsystem 620 and an optical sensor 622 such as a charged coupled device (CCD) or a complementary metal-oxide semiconductor (CMOS) optical sensor can facilitate camera functions such as recording photographs and video clips.
  • CCD charged coupled device
  • CMOS complementary metal-oxide semiconductor
  • Wireless communication subsystems 624 can include 802.x or Bluetooth transceivers as well as optical transceivers such as infrared.
  • Wired communication system can include a port device such as a Universal Serial Bus (USB) port or some other wired port connection that can be used to establish a wired coupling to other computing devices such as network access devices, personal computers, printers, displays, or other processing devices capable of receiving or transmitting data.
  • USB Universal Serial Bus
  • communication subsystem 624 may depend on the communication network or medium over which the device is intended to operate.
  • a device may include wireless communication subsystem designed to operate over a global system for mobile communications (GSM) network, a GPRS network, an enhanced data GSM environment (EDGE) network, 802.x communication networks, code division multiple access (CDMA) networks, or Bluetooth networks.
  • GSM global system for mobile communications
  • EDGE enhanced data GSM environment
  • 802.x communication networks such as a base station for other wireless devices.
  • Communication subsystems can also allow the device to synchronize with a host device using one or more protocols such as TCP/IP, HTTP, or UDP.
  • Audio subsystem 626 can be coupled to a speaker 628 and one or more microphones 630 to facilitate voice-enabled functions. These functions might include voice recognition, voice replication, or digital recording. Audio subsystem 626 in conjunction may also encompass traditional telephony functions.
  • I/O subsystem 640 may include touch controller 642 and/or other input controller(s) 644.
  • Touch controller 642 can be coupled to a touch surface 646.
  • Touch surface 646 and touch controller 642 may detect contact and movement or break thereof using any of a number of touch sensitivity technologies, including but not limited to capacitive, resistive, infrared, or surface acoustic wave technologies.
  • Other proximity sensor arrays or elements for determining one or more points of contact with touch surface 646 may likewise be utilized.
  • touch surface 646 can display virtual or soft buttons and a virtual keyboard, which can be used as an input/output device by the user.
  • Other input controllers 644 can be coupled to other input/control devices 648 such as one or more buttons, rocker switches, thumb-wheels, infrared ports, USB ports, and/or a pointer device such as a stylus.
  • the one or more buttons can include an up/down button for volume control of speaker 628 and/or microphone 630.
  • device 600 can include the functionality of an audio and/or video playback or recording device and may include a pin connector for tethering to other devices.
  • Memory interface 602 can be coupled to memory 650.
  • Memory 650 can include high-speed random access memory or non-volatile memory such as magnetic disk storage devices, optical storage devices, or flash memory.
  • Memory 650 can store operating system 652, such as Darwin, RTXC, LINUX, UNIX, OS X, ANDROID, WINDOWS, or an embedded operating system such as VxWorks.
  • Operating system 652 may include instructions for handling basic system services and for performing hardware dependent tasks.
  • operating system 652 can include a kernel.
  • Memory 650 may also store communication instructions 654 to facilitate communicating with other mobile computing devices or servers. Communication instructions 654 can also be used to select an operational mode or communication medium for use by the device based on a geographic location, which could be obtained by the GPS/Navigation instructions 668.
  • Memory 650 may include graphical user interface instructions 656 to facilitate graphic user interface processing such as the generation of an interface; sensor processing instructions 658 to facilitate sensor-related processing and functions; phone instructions 660 to facilitate phone-related processes and functions; electronic messaging instructions 662 to facilitate electronic-messaging related processes and functions; web browsing instructions 664 to facilitate web browsing-related processes and functions; media processing instructions 666 to facilitate media processing-related processes and functions; GPS/Navigation instructions 668 to facilitate GPS and navigation-related processes, camera instructions 670 to facilitate camera-related processes and functions; and instructions 672 for any other application that may be operating on or in conjunction with the mobile computing device.
  • Memory 650 may also store other software instructions for facilitating other processes, features and applications, such as applications related to navigation, social networking, location- based services or map displays.
  • Each of the above identified instructions and applications can correspond to a set of instructions for performing one or more functions described above. These instructions need not be implemented as separate software programs, procedures, or modules. Memory 650 can include additional or fewer instructions. Furthermore, various functions of the mobile device may be implemented in hardware and/or in software, including in one or more signal processing and/or application specific integrated circuits.
  • a computer system that includes a back-end component, such as a data server, that includes a middleware component, such as an application server or an Internet server, or that includes a front-end component, such as a client computer having a graphical user interface or an Internet browser, or any combination of the foregoing.
  • the components of the system can be connected by any form or medium of digital data communication such as a communication network.
  • Some examples of communication networks include LAN, WAN and the computers and networks forming the Internet.
  • the computer system can include clients and servers.
  • a client and server are generally remote from each other and typically interact through a network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client- server relationship to each other.
  • One or more features or steps of the disclosed embodiments may be implemented using an API that can define on or more parameters that are passed between a calling application and other software code such as an operating system, library routine, function that provides a service, that provides data, or that performs an operation or a computation.
  • the API can be implemented as one or more calls in program code that send or receive one or more parameters through a parameter list or other structure based on a call convention defined in an API specification document.
  • a parameter can be a constant, a key, a data structure, an object, an object class, a variable, a data type, a pointer, an array, a list, or another call.
  • API calls and parameters can be implemented in any programming language.
  • the programming language can define the vocabulary and calling convention that a programmer will employ to access functions supporting the API.
  • an API call can report to an application the capabilities of a device running the application, such as input capability, output capability, processing capability, power capability, and communications capability.
  • FIG. 7 A illustrates an exemplary wearable threshold level database 26 as stored by an exemplary wearable device 10.
  • the wearable threshold level database may be stored in the memory of the wearable device.
  • the wearable threshold level database may store the user's base reading rates, threshold levels, threshold reading rates, and threshold alert actions/messages for the one or more sensors of the wearable device. This can be filled in if the user selects a preset choice using the wearable download software (see e.g., FIG. 4A, FIG. 4B, FIG. 4C) or can be filled in manually through the wearable sensor setting software (see e.g., FIG. 3A and FIG. 3B).
  • the exemplary wearable threshold level database 26 of FIG. 7 A includes base reading rates, threshold levels, threshold reading rates, and threshold alert actions/messages for each of four sensors (pulse, temperature, blood pressure, and respiration).
  • the base reading rate for all four sensors is "every 10 minutes.”
  • the threshold level is different for each sensor (such as pulse: over 110 bpm; temperature: over 101 degrees Fahrenheit; blood pressure: over 140/80 mm Hg; respiration: over 23 respirations per minute, for example).
  • the threshold reading rate is different for each sensor (such as pulse: every 10 seconds; temperature: every 1 minute; blood pressure: every 30 seconds; respiration: every 10 seconds, for example).
  • Each includes a message to be displayed by the user alert GUI indicating that the relevant threshold level has been exceeded, and that the wearable device is transitioning to use the threshold reading rate for the relevant sensor.
  • this had been a preset settings choice (e.g., it is similar, though slightly different than the "40's Male" preset settings choice of FIG. 5A)
  • the user of the wearable device could have selected the preset settings choice in the preset choices GUI (e.g. > the exemplary preset choices GUI of FIG. 4 A) and selected the "download" button at the preset sensor selection GUI (e.g., the exemplary preset sensor selection GUI of FIG. 4B).
  • the user could have done so by entering them using the user sensor selection GUI (e.g., the exemplary user sensor selection GUI of FIG. 3A) and the user sensor settings GUI (e.g., the exemplary user sensor settings GUI of FIG. 3B).
  • the user sensor selection GUI e.g., the exemplary user sensor selection GUI of FIG. 3A
  • the user sensor settings GUI e.g., the exemplary user sensor settings GUI of FIG. 3B
  • FIG. 7B illustrates an exemplary network download database 36 as stored by an exemplary network 30.
  • the network download database may be stored at the network, and may contain a listing of the various preset settings choices, where each preset settings choice is associated with a particular network threshold level database that stores relevant data (e.g., base reading rate, threshold level, threshold reading rate, threshold alert action/message) for a set of sensors.
  • the network download databases may be stored at a single "central" network threshold level database.
  • the user of the wearable device may start the wearable device and have it execute the user sensor selection GUI (e.g., the exemplary user sensor selection
  • the wearable device may have a way to initially start in the preset choices GUI (e.g. ⁇ the exemplary preset choices GUI of FIG. 4A). Either way, the listing of available preset choices that is displayed at the preset choices GUI (e.g. > the exemplary preset choices
  • GUI of FIG. 4A can be obtained directly from the network download database.
  • the network receives this choice and uses the network download database to determine where additional information about that particular choice is stored (e.g., the "college athlete” choice is associated with the “college athlete” network threshold level database, the "heart disease” choice is associated with the “heart disease” network threshold level database, the "diabetic” choice is associated with the “diabetic” network threshold level database, the "elderly” choice is associated with the “elderly” network threshold level database, the "40' s male” choice is associated with the “40' s male” network threshold level database).
  • This information is then transferred from the appropriate network threshold level database directly to the wearable threshold level database of the wearable device.
  • FIG. 8A illustrates an exemplary wearable sensor database 28 as stored by an exemplary wearable device 10.
  • the exemplary wearable sensor database includes sensor measurement reading data taken under "normal” or "healthy” conditions when the user's sensor value is below the threshold level ⁇ e.g., pulse under a threshold level of 110 beats per minute). Thus, these measurements are taken at a base reading rate (e.g., every 10 minutes).
  • he exemplary wearable sensor database includes sensor data from a pulse sensor (measurements ranging from 85 bpm to 86 bpm up to 9:15, then jumping to 112 bpm at 9:25), a blood pressure sensor (measurements ranging from 120/80 mm Hg to 127/80 mm Hg), a respiration sensor (measurements ranging from 15 respirations/minute to 21 respirations/minute), and a temperature sensor (measurements ranging from 98.6 degrees Fahrenheit to 98.9 degrees Fahrenheit).
  • a pulse sensor measurements ranging from 85 bpm to 86 bpm up to 9:15, then jumping to 112 bpm at 9:25
  • a blood pressure sensor measurements ranging from 120/80 mm Hg to 127/80 mm Hg
  • a respiration sensor measurements ranging from 15 respirations/minute to 21 respirations/minute
  • a temperature sensor measurements ranging from 98.6 degrees Fahrenheit to 98.9 degrees Fahrenheit
  • FIG. 8B illustrates an exemplary wearable threshold sensor data database 32 as stored by an exemplary wearable device 10.
  • the exemplary wearable threshold sensor data database includes sensor measurement reading data taken under "potential danger" conditions when the user's sensor value exceeds the threshold level (e.g., pulse over a threshold level of 110 beats per minute). Thus, these measurements are taken at a threshold reading rate (e.g., every 10 seconds).
  • the exemplary wearable threshold sensor data database includes data only from the pulse sensor, because the pulse sensor is the only sensor whose threshold level (e.g. ⁇ over 110 bpm as in the exemplary threshold level of FIG. 7 A) has been exceeded.
  • the pulse measurement values range from 112 bpm to 121 bpm.
  • FIG. 9 is a flow diagram illustrating exemplary operations of an exemplary wearable sensor settings software 44 of an exemplary wearable device 10, according to an embodiment.
  • the exemplary wearable sensor settings software of the exemplary wearable device begins by displaying the user sensor selection GUI on the display of the wearable device.
  • the wearable device then allows the user to select "Download Preset Settings" or to select a particular sensor.
  • the wearable device may poll the particular hardware interface through which the user may make this choice (e.g., touchscreen, buttons, selection wheel, keyboard, mouse). If the user selected "download preset settings," then the wearable device executes the wearable download software.
  • the wearable device may return to polling for an input. If the user did not select "download preset settings," and the user selected a particular sensor, then the wearable device displays the user sensor settings GUI for the particular sensor on the display of the wearable device. The wearable device then allows the user to select the settings for the particular sensor using the user sensor settings GUI, where the user may then select "save" to complete and save their settings. The wearable device then saves the user selected settings for the particular sensor into the wearable threshold level database of the wearable device. The wearable device may then execute the wearable device monitoring software.
  • FIG. 9 shows a particular order of operations performed by certain embodiments, it should be understood that such order is exemplary and that alternative embodiments can perform the operations in a different order, combine certain operations, overlap certain operations, and operate according to many other configurations.
  • FIG. 10 is a lane-based flow diagram illustrating exemplary operations of an exemplary wearable download software 42 of an exemplary wearable device 10 interacting with an exemplary network download software 38 of an exemplary network 30, according to an embodiment.
  • the exemplary wearable download software of the exemplary wearable device begins by displaying the preset choices GUI on the display of the wearable device.
  • the wearable device then allows the user to select a preset settings choice through the preset choices GUI. Such a selection then opens the preset sensor selection GUI for the particular preset settings choice that the user selected.
  • the wearable device may then allow the user to select a "download" option at the preset sensor selection GUI. If the user selects the "download” option, the wearable device then transmits the particular preset settings choice that the user selected to the network.
  • the network download software of the network then receives the particular preset settings choice that the user selected, along with a request for downloading data associated with the particular preset settings choice (e.g., base reading rates, threshold levels, threshold reading rates threshold alert actions/messages).
  • the network download software compares the particular preset settings choice with the entries of the network download database of the network to find a matching entry, which specifies a particular network threshold level database where additional data associated with the particular settings choice (e.g., base reading rates, threshold levels, threshold reading rates threshold alert actions/messages) can be found. This data (and in some embodiments, the entire matching network threshold level database) is then sent to the wearable device.
  • the wearable device's wearable download software then receives the data (and in some embodiments, the entire matching network threshold level database) and saves this data into the wearable threshold level database of the wearable device.
  • the wearable device then executes the wearable monitoring software of the wearable device.
  • FIG. 10 shows a particular order of operations performed by certain embodiments, it should be understood that such order is exemplary and that alternative embodiments can perform the operations in a different order, combine certain operations, overlap certain operations, and operate according to many other configurations.
  • FIG. 11 A is a flow diagram illustrating exemplary operations of an exemplary wearable monitoring software 46 of an exemplary wearable device 10.
  • the exemplary wearable monitoring software of the exemplary wearable device begins by taking wearable readings with the sensor(s) of the wearable device and the clock of the wearable device. The wearable device then saves the readings from the sensor(s) and clock into the wearable sensor database. The wearable device then retrieves the most recent sensor data entry from the wearable sensor database (z ' .e. 4 the one that was just saved). The wearable device then compares the most recent data entry from the wearable sensor database with the wearable threshold level database. If there is a match, in the wearable threshold level database, the wearable device then executes the wearable action software (see FIG. 11B).
  • the wearable device then turns the sensor(s) of the wearable device into "sleep" mode.
  • the wearable device then waits for the period of time listed in the "Base reading rate” as listed in the wearable threshold level database (e.g., 10 minutes).
  • the wearable device then turns the sensor(s) wearable device back "on” or into an "active” mode, after which the wearable device is ready to return to taking wearable readings with the sensor(s) of the wearable device and the clock of the wearable device.
  • 11B is a flow diagram illustrating exemplary operations of an exemplary wearable action software 50 of an exemplary wearable device 10.
  • the exemplary wearable action software of the exemplary wearable device begins by vibrating the wearable device.
  • the wearable device displays the user alert GUI 50a on the display of the wearable device.
  • the wearable device executes the appropriate threshold alert action for the matching data entry in the wearable threshold level database.
  • the wearable device then executes the wearable threshold monitoring software.
  • FIG. 11 A and FIG. 11B show a particular order of operations performed by certain embodiments, it should be understood that such order is exemplary and that alternative embodiments can perform the operations in a different order, combine certain operations, overlap certain operations, and operate according to many other configurations.
  • FIG. 12 is a flow diagram illustrating exemplary operations of an exemplary wearable threshold monitoring software 48 of an exemplary wearable device 10.
  • the exemplary wearable monitoring software of the exemplary wearable device begins by taking wearable readings with the sensor(s) of the wearable device that reached/exceeded a threshold level and the clock of the wearable device. The wearable device then saves the readings from the sensor(s) that reached/exceeded a threshold level and clock into the wearable sensor database. The wearable device then retrieves the most recent sensor data entry from the wearable sensor database (z ' .e. 4 the one that was just saved). The wearable device then may display the most recent sensor data entry form the wearable threshold sensor data database at the user alert GUI.
  • the wearable device compares the most recent data entry from the wearable sensor database with the wearable threshold level database. If there is no match in the wearable threshold level database, the wearable device then executes the wearable monitoring software (see FIG. 11A). [00121] If there was a match when the wearable device compared the most recent data entry from the wearable sensor database with the wearable threshold level database, the wearable device then turns the sensor(s) of the wearable device into "sleep" mode. The wearable device then waits for the period of time listed in the "Threshold reading rate” as listed in the wearable threshold level database (e.g., 10 seconds). The wearable device then turns the sensor(s) wearable device back “on” or into an "active” mode, after which the wearable device is ready to return to taking wearable readings with the sensor(s) of the wearable device and the clock of the wearable device.
  • FIG. 12 shows a particular order of operations performed by certain embodiments, it should be understood that such order is exemplary, and that alternative embodiments can perform the operations in a different order, combine certain operations, overlap certain operations, and operate according to many other configurations.
  • FIG. 13 illustrates an embodiment of a method 1300 for automatically modifying a sensor sampling rate based on a relationship of the sensor's data to a threshold, as described or otherwise envisioned herein.
  • a health wearable system 100 comprising a wearable device 10 connected by a communications network 40 to a network 30.
  • the wearable device 10 may include a plurality of wearable sensors 12, an OS, a power source 20, a vibrator 22, a wearable communications module 18, a display 14, a clock 16, and a memory 24 further comprising a wearable threshold level database, a wearable sensor database, a wearable threshold sensor data database, a wearable monitoring software, a wearable threshold monitoring software, a wearable action software further comprising a user alert GUI, a wearable sensor setting software further comprising a user sensor selection GUI and a user sensor settings GUI, and a wearable download software further comprising a preset choices GUI, a preset sensor selection GUI, and a preset sensor setting GUI.
  • many other embodiments and configurations of wearable device 10 are possible.
  • the network 30 may comprise, according to an embodiment, a network communications module, a network threshold level database 34, a network download database 36, and a network download software 38, among many other possible components and/or modules.
  • the wearable sensor setting software may execute in order to display the User sensor selection GUI on the wearable display, to allow the user to select "Download Preset Settings" or to select a sensor, to poll the user sensor selection GUI for user selecting "Download Preset
  • the wearable download software may execute in order to display one or more preset choices on the wearable display, to allow the user to select a preset choice on the download GUI to open the preset sensor selection GUI for the selected preset choice, to allow the user to select 'Download" on the preset sensor selection GUI, to send the selected preset choice for download to the network download software, to receive from the network download software the matching network threshold level database and to save to the wearable threshold level database, to execute the wearable monitoring software.
  • the network download software may execute in order to receive selected preset choice for download from the wearable download software, to compare selected preset choice with the network download database to find the matching network threshold level database, to copy and send the matching network threshold level database to the wearable download software.
  • the wearable monitoring software may execute in order to take wearable sensor and wearable clock readings, to save to the wearable sensor database, to retrieve the most recent sensor data entry from the wearable sensor database, to compare the most recent data entry from the wearable sensor database with the wearable threshold level database, to determine if there is a match, if yes to execute the wearable action software, if not to turn the wearable sensors to Sleep Mode, to wait for the time equal to "Base Reading Rate" as listed in the wearable threshold level database, to turn wearable sensors to Active Mode, and to loop back to take wearable sensor and wearable clock readings.
  • the wearable action software may execute to vibrate the wearable device, to display the user alert GUI on the wearable display, to execute the appropriate threshold alert action for the matching data entry in the wearable threshold level database, and to execute the wearable threshold monitoring software.
  • the wearable threshold monitoring software may execute in order to take readings of the wearable clock and the wearable sensor that reached the threshold level, to save to the wearable threshold sensor data database, to retrieve the most recent sensor data entry from the wearable threshold sensor data database, to display the most recent sensor data entry form the wearable threshold sensor data database on the user alert GUI, to compare the most recent data entry from the wearable threshold sensor data database with the wearable threshold level database, to determine if there is a match, if not to execute the wearable monitoring software, if yes to wait for the time equal to "Threshold Reading Rate" as listed in the wearable threshold level database for the wearable sensor that reached the threshold level and to loop back to take readings of the wearable clock and the wearable sensor that reached the threshold level.
  • FIG. 13 shows a particular order of operations performed by certain embodiments, it should be understood that such order is exemplary, and that alternative embodiments can perform the operations in a different order, combine certain operations, overlap certain operations, and operate according to many other configurations.
  • a wearable device 10 is provided, which may be any of the wearable devices described or otherwise envisioned herein.
  • wearable device 10 may comprise one or more sensors 12, a display 14, a processor 52, and a memory 24, among many other components.
  • the one or more sensors may, for example, obtain data about blood pressure, heart rate, pulse, blood oxygen, body temperature, blood sugar, blood glucose, movement, insulin, vitamin levels, respiratory rate, heart sound, breathing sound, skin moisture, sweat detection, sweat composition, nerve firings, or similar health measurements, among many other options.
  • a sensor data threshold is established, downloaded, entered, programmed, or otherwise provided and/or stored in the wearable device 10.
  • the sensor data threshold will be utilized by the wearable device to determine when to modify the sampling rate of the sensor, as described in detail below.
  • the user may determine, via a user interface, one or more thresholds. Thresholds may also be a factory setting. A threshold may also vary depending on the location, time, date, activity, environment, or other factor. Accordingly, a threshold setting need not be fixed but can vary by a percentage, range, or other amount depending on internal or external variables.
  • sensor 12 obtains sensor data.
  • the sensor may obtain sensor data at a first frequency, and/or the processor may receive or request the obtained sensor data at a first frequency (or a different initial frequency).
  • This first or default or initial frequency may be a default frequency set by either the sensor or the processor.
  • the sensor may obtain heart rate data every 30 seconds as a default.
  • the obtained sensor data is compared to a predetermined sensor data threshold.
  • the predetermined sensor data threshold may be a threshold determined or set by the user in step 1405.
  • the threshold may also be a factory setting, or may be determined experimentally, or learned, or otherwise set or provided. If the obtained sensor data exceeds the predetermined sensor data threshold (or meets the predetermined sensor data threshold, depending on the triggering parameters of the wearable device), then the processor or sensor will change the first frequency to a sampling rate of a second frequency at step 1440 of the method, where the second frequency is typically faster than the first frequency.
  • the obtained sensor data may obtain heart rate data every 30 seconds as a default, and the predetermined sensor data threshold may be 100 beats per minute (bpm).
  • the predetermined sensor data threshold is exceeded, triggering a change in the first frequency of every 30 seconds.
  • a new frequency of every 10 seconds is utilized as the new second frequency.
  • the second frequency can simply be a predetermined frequency, a percentage increase, or another programmed, predetermined, derived, and/or learned frequency.
  • sensor 12 obtains sensor data at the second frequency.
  • the sensor may obtain heart rate data every 10 seconds.
  • the obtained sensor data is compared to the predetermined sensor data threshold.
  • the predetermined sensor data threshold in the heart rate example is 100 bpm. If the obtained sensor data falls below the predetermined sensor data threshold (or meets the predetermined sensor data threshold, depending on the triggering parameters of the wearable device), then the processor or sensor will return to a sampling rate of the first frequency. If the obtained sensor data does not fall below the predetermined sensor data threshold, then the rate will remain at the second frequency.
  • the lower sampling rate at the first frequency has numerous benefits, including preserving battery power. The device preserves battery when data is below the threshold and is therefore less important, but can trigger collection of important data at a higher rate only when needed. As a result, battery power is conserved and extended as further described herein.
  • step 1420 The method then returns to step 1420 and sensor 12 obtains sensor data at the first frequency.
  • step 1460 of the method if the sensor data is compared to a second, higher predetermined sensor data threshold and that threshold is exceeded, then the processor or sensor can change the second frequency to a sampling rate of a third frequency, where the third frequency is typically faster than the second frequency. For example, if the second frequency is every 10 seconds and a bpm of 121 exceeds a second higher predetermined sensor data threshold of 120, then the processor or sensor can change the second frequency to a third frequency of every 1 second.
  • Embodiments also relate to an apparatus for performing the operations herein.
  • Such a computer program is stored in a non-transitory computer readable medium.
  • a machine-readable medium includes any mechanism for storing information in a form readable by a machine (e.g., a computer).
  • a machine-readable (e.g., computer-readable) medium includes a machine (e.g., a computer) readable storage medium (e.g., read only memory (“ROM”), random access memory (“RAM”), magnetic disk storage media, optical storage media, flash memory devices).
  • processing logic that comprises hardware (e.g. circuitry, dedicated logic, etc.), software (e.g., embodied on a non-transitory computer readable medium), or a combination of both.
  • processing logic comprises hardware (e.g. circuitry, dedicated logic, etc.), software (e.g., embodied on a non-transitory computer readable medium), or a combination of both.

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Abstract

L'invention concerne un dispositif vestimentaire pouvant comprendre un ou plusieurs capteurs, qu'il peut utiliser pour prendre périodiquement des mesures de capteurs de paramètres physiologiques (<i />par ex. le rythme cardiaque) d'un utilisateur du dispositif vestimentaire selon un cadence de mesure de base (<i />par ex. toutes les 10 minutes). Chaque mesure de capteur est comparée à un niveau seuil prédéterminé (e.gpar ex. prédéterminé au niveau du dispositif vestimentaire ou d'un réseau). Si une mesure dépasse le niveau seuil prédéterminé, le dispositif vestimentaire génère une alerte au niveau du dispositif vestimentaire et commence à prendre périodiquement des mesures de capteur à une cadence seuil de mesure plus rapide (par ex. toutes les 10 secondes) à l'aide du capteur dont la mesure a dépassé la limite seuil. Si une nouvelle mesure tombe au-dessous de la limite seuil, le dispositif vestimentaire peut revenir à l'utilisation de la cadence de mesure de base. Ainsi, l'énergie de la batterie utilisée par les capteurs du dispositif vestimentaire est économisée à moins qu'une mesure de capteur indique qu'ils sont nécessaires en raison d'un risque potentiel pour la santé.
PCT/IB2016/051595 2015-03-24 2016-03-22 Gestion intelligente d'alimentation de capteurs pour article vestimentaire de santé WO2016151479A1 (fr)

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