WO2018211519A1 - Moniteur d'activité physique et de paramètres vitaux portable sans fil - Google Patents

Moniteur d'activité physique et de paramètres vitaux portable sans fil Download PDF

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Publication number
WO2018211519A1
WO2018211519A1 PCT/IN2017/050280 IN2017050280W WO2018211519A1 WO 2018211519 A1 WO2018211519 A1 WO 2018211519A1 IN 2017050280 W IN2017050280 W IN 2017050280W WO 2018211519 A1 WO2018211519 A1 WO 2018211519A1
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WO
WIPO (PCT)
Prior art keywords
user
data
shows
section
health
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Application number
PCT/IN2017/050280
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English (en)
Inventor
Viswanthan Vivek
Mani Sridharan
Anees Mohmed
Gowrisankaran Ketharaman
Jayachandran Anantharaman
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American Megatrends India Private Limited
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Priority to PCT/IN2017/050280 priority Critical patent/WO2018211519A1/fr
Publication of WO2018211519A1 publication Critical patent/WO2018211519A1/fr

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Classifications

    • 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
    • A61B5/0022—Monitoring a patient using a global network, e.g. telephone networks, internet
    • 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
    • A61B5/0205—Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
    • A61B5/02055—Simultaneously evaluating both cardiovascular condition and temperature
    • 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
    • A61B5/02108—Measuring pressure in heart or blood vessels from analysis of pulse wave characteristics
    • A61B5/02125—Measuring pressure in heart or blood vessels from analysis of pulse wave characteristics of pulse wave propagation time
    • 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
    • A61B5/1118—Determining activity level
    • 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
    • A61B5/1455—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 using optical sensors, e.g. spectral photometrical oximeters
    • A61B5/14551—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 using optical sensors, e.g. spectral photometrical oximeters 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
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
    • 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

Definitions

  • the current invention is intended to be an integrated, portable, wireless health and fitness tracker which interface's to a mobile or desktop computing device using a wireless interface to send and receive the collected and compute data.
  • Figure 4 shows the cross-sectional and three-dimensional figures of the invention.
  • the invention has a two electrodes for electrocardiogram measurement (section 12, section 19, section 31 ), optical and infrared detectors for pulse wave detection (28,29), 3-axis accelerometer, gyroscope, magnetometer and an Infrared sensor (18).
  • the whole sensor arrangement is placed and soldered in a Printed Circuit Board (PCB) ( Figure 5) with the mechanical casing (3) shown in Figure 1 covering the PCB and other components like the battery, Blood Oxygen sensors and connectors.
  • PCB Printed Circuit Board
  • Figure 2 shows an exemplary embodiment of the device in operation to measure the health vital parameters where in Figure 2 shows the left thumb along with right Thumb finger (20) pressing against the two leads of the electrocardiogram and the position of the right index finger (22) pressing against the optical sensor. This way both the electrocardiogram and pulse waves simultaneously measured and metrics sent for processing.
  • Figure 3 shows the measurement of human temperature where the invention is held against the forehead of the user. Using Infrared waves the temperature is measured.
  • Figure 5 shows the block diagram of the hardware section of the invention in which the components are soldered on printed circuit board with the 1 -lead ECG electrodes (41 ) connected to an ECG signal conditioning circuit (50), Optical sensor (42) its respective signal conditioning section (51 ), Infrared temperature sensor (43) with its signal conditioning section (46), 9-Axis accelerometer, gyroscope and magnetometer (44) with its signal conditioning section (45) , battery and charging section (47), wireless communication section (49) are connected to a microcontroller with non-volatile memory like flash memory and Random access memory (48) forms the hardware part of the invention.
  • a microcontroller with non-volatile memory like flash memory and Random access memory (48) forms the hardware part of the invention.
  • Figure 6 shows yet another exemplary embodiment of the invention in measuring blood oxygen saturation waveform where to accommodate multiple finger sizes a single spring (section4, section 9) is kept allowing for vertical movement along with a photo diode (optical sensor) (section 29 of fig 1 ), Red/Infrared LED
  • Section 10 of Figure 6 shows the gap formed on the top sensor portion on the slider (6) containing the Red and IR leds moving in a vertical direction to accommodate different finger sizes using a single spring (section 4, section 9) mounted as part of the mechanical casing.
  • Figure 7 shows another perspective of the invention with section (19) showing the placement of AgCI ECG electrodes providing a contoured area to get maximum grip for the fingers.
  • Section (18) shows the front side view of the Infrared temperature sensor.
  • Figures 8,9 and 10 show illustrative embodiment of the invention in its ability to detect different forms of physical activities.
  • Figure 10 shows the typical operation of the invention device 26 where the invention rests in the pant pocket of the user tracking activities like sitting, standing, walking and climbing.
  • Figure 8 section 24 shows the invention worn by the user in user's arm tracking the different physical exercise which the user performs.
  • Figure 9 shows another illustrative
  • invention device 32 tracks the user's running form.
  • the method by which the invention performs these exemplary embodiments of user activity tracking is shown in Figure 19.
  • the Accelerometer data (80), Gyroscope data (81 ) and Magnetometer data (82) are received, passed through a digital filter for signal conditioning.
  • a series of key markers are extracted from the input digital signals (83) and an example of the markers which could also be called as "features" are the mean, different ratios of root mean square, frequency bins and ranges, peak heights and correlation values of the signal.
  • features are the mean, different ratios of root mean square, frequency bins and ranges, peak heights and correlation values of the signal.
  • There are then fed into a machine learning classifier which finds the weights to solve these set of nonlinear equations.
  • the output of this classifier block could then be used by the invention to predict the different activities which the user is performing.
  • Figure 1 1 shows another exemplary embodiment for detecting human
  • the Infrared sensor 27 is placed in the middle housing covered by the metal heatsink (30) and the outer plastic which gives it the ability to measure temperature from the forehead accurately.
  • Figure 13 shows the overall block diagram of the invention (52) communicating with mobile/desktop computing unit (53) wirelessly and via the mobile/desktop computing unit which could be a Desktop PC , Mobile phone or a Tablet and communicate to a central server or cloud storage (54) using either wired or wireless internet .
  • the mobile/desktop computing unit (53) in addition to sending captured data to a central server also acts as a the Display section to display the results of the vitals and health data captured, input section to enter the users details like age, height, gender, weight and other related parameters, has an algorithm section to compute the different health and fitness conditions based on the data received from the invention and has a local database to store vitals, fitness and other related parameter's.
  • the invention works in tandem with the mobile/desktop computing unit for operation and relies on the mobile/desktop computing unit for configuration, synchronizing the data and transmitting vitals and health parameters.
  • FIG. (1 ) shows the Pulse Wave detector with Visual wavelength and Infrared wavelength Light Emitting Diodes LED's (28) and Optical detector also knows as a Photo Diode (29) working on transmittance principle where the reflected light from the LED's hitting on the user's finger is captured and analyzed.
  • the Visual and Optical LED's are switched on and off alternatively every few milliseconds and the reflected light along with the ambience is captured both in Visual wavelengths and Infrared wavelengths.
  • the captured light is available both in the visible light spectrum (74) and Infrared spectrum (75). This is then passed through a band pass filter to remove noise to get Filtered visible light spectrum signal (76) and infrared light spectrum signal (77).
  • This signal contains both DC and AC components and different signal characteristics based on Photo Diode response curves. Hence the signal is normalized and the final response curve is calculated based on the formula,
  • Ratio values are compared with a pre-existing lookup table and the Blood Oxygen values are calculated
  • the ECG signal (61 ) is received from the electrodes is processed to perform Heart Rate Variability analysis of the user.
  • the original signal is first normalized (62) and then passed through a cascaded digital band pass filter that is a high pass filter(63) followed by a low pass filter(64).
  • the filtered signal is then differentiated (Figure 15, 65) to obtain information about the slope of the QRS wave which is followed by squaring stage (66) which intensifies the slope of the slope of the frequency response curve of the derivative and restricts false T waves. This is followed by the signal integrated over a Moving Window (67).
  • the resultant signal contains information of both the slope and width of the QRS complex.
  • Pulse Transit Time is the time taken between the Peak of the R wave (72) and the peak of the Pulse Wave (70) which corresponds to the time takes for the blood to flow from heart to the Finger. This PTT is directly proportional to Blood Pressure using the relation,
  • the factors which cause the constant differences are due to the height and age of the user.
  • PTT Pulse Transit time
  • BP Pulse Transit time
  • the polynomial regression relation is then stored in the software of the mobile/desktop computing device and when the user measures the Blood Pressure from the gadget the PTT(71 ) is calculated and fed into the pre-calculated regression formula to find the Systole and the Diastole values which corresponds to the Blood Pressure of the Individual.
  • Figure 18 shows the signal form laboratory conditions on the captured heart signal in two conditions where time between the two R-peaks is calculated referred to RR time interval.
  • first condition (78) in figure 13 shows the maximum time difference between two R-peaks and the second condition (79) shows very less time difference between two R-peaks for same average heart rate of 72 Beats per minute. This time difference between two peaks is called as Heat Rate Variability and measured by the invention.
  • From the calculated heart rate variability if the variability is high between subsequent readings then user has less stress and if the variability is less between subsequent readings then the user more stress. This value is compared with the calculated blood pressure from Figure 16 to give the stress level of the user. Also the stress level is mapped to different time intervals and using location services from the connected computing device (figure 23, 103) to the time and place where the user had maximum stress could be computed and displayed the user for further analysis.
  • Figure 21 shows yet another exemplary embodiment of the invention in detecting common conditions like Atrial Fibrillation from the ECG waveform.
  • the filtered ECG signals (87) and accelerometer sensor values (88) are fed to the feature extractor module (89).
  • the markers from the filtered signal typically used in classifying an ECG signal is extracted.
  • Some of the examples of markers which could be extracted are N-N intervals which is the distance between to R-waves, frequency domain components, QRS segment duration and decomposed signals with wavelets.
  • All these signals are fed into a series of multi layered neural networks also called sometimes referred to "Deep neural network or Deep learning" which then learns to classify the input signal into one three types which are Normal ECG waveform, ECG waveform with Atrial Fibrillation or unclassified type which are waveforms which the system was not able to classify into either Normal or AFib.
  • Deep neural network or Deep learning a series of multi layered neural networks also called sometimes referred to "Deep neural network or Deep learning” which then learns to classify the input signal into one three types which are Normal ECG waveform, ECG waveform with Atrial Fibrillation or unclassified type which are waveforms which the system was not able to classify into either Normal or AFib.
  • Figure 22 shows another exemplary embodiment of the invention by the ability to share the captured data with other users with different levels of data sharing ability.
  • the invention (52) transmits the data mobile/desktop computing unit (53) which is then via Internet or Intranet transmits the data to an external server or a series of servers connected together providing a cloud based storage and processing mechanism (54).
  • Data from multiple users are collected and stored in the database (94) and each individual's (96) has the option for selecting different sharing levels. For example the user can select from a list of people using the same type program running in the computing device as belonging to family (97) or friends (98) with the ability of user to select and send the type and extent of data to be shared with each member in the list.
  • the program running in the Family list (97) or Friends list (98) would also have the ability synchronize the data records periodically with the ability for offline viewing.
  • the present invention from the program running in the mobile/desktop computing unit (53) provides the ability to monitor trends and diagnose conditions as then when they appear based on past data records.
  • referring figure 16 shows four different example graphs based on data captured by the invention.
  • the first graph (101 ) shows Heart Rate Variability (HRV) called as stress plotted against time with the readings measured by the invention.
  • HRV Heart Rate Variability
  • the HRV is less in the morning and in evening, which is captured by the program running on the computing device and displayed to the user.
  • the second graph (102) shows Arrhythmia also called as Cardiac dysrhythmia is a condition where the electrical activity is not normal which is that the heart activity could be either faster or slower which is captured by the inventions electrocardiograph. This data is analyzed to show the time and place where the Arrhythmia occurred and display the type of arrhythmia.
  • Another example of trend monitoring is from the third graph (103) which shows the plot of increased stress levels (103), blood pressure (104) and physical activity (105) captured by the invention where the stress increase is inversely proportional to the decrease in physical activity (105).
  • Yet another example of trend monitoring would be sleep monitoring from the fourth graph where the user wears the invention on the wrist and goes to sleep and the invention monitors both the blood oxygen (106) and movement (107) and displays the sleep pattern and sleep duration of the user.
  • Figure 1 shows the overall perspective of the invention with section 1 inside figure 1 showing the Pulse Oximeter detection chamber, section 2 shows the plastic cap which the user opens to insert his/her finger and section 3 shows the outer body design of the product.
  • Section 28 is the red / infrared LED for Sp02 detection and section 29 is the optical sensor.
  • Section 36 shows the hinge for the plastic cap to open and close.
  • Figure 2 shows the invention in usage where section 20 shows two fingers of the hand pressing on the two ECG electrodes and in section 22 the index finger of the hand in the pulse oximeter detection chamber pressing on the optical sensors. Section 21 shows the plastic cap in the open condition.
  • FIG. 3 section 23 shows the invention pressed against the forehead of the user to measure the temperature.
  • the section 39 and 40 show the position of the ECG Electrodes and the plastic cap of Sp02 detection chamber in closed condition respectively.
  • FIG 4 shows the 3-Dimentional cross-section of the gadget with section 1 1 showing the Blood oxygen sensor (Sp02) protection cap in closed condition, section 12 showing the electrodes used for Electrocardiograph measurement and section 13 showing opening for the USB connector, section 16 and 17 showing the indicator led for power and wireless connection respectively and section 14 and 15 showing the switch for turning on the power and wireless circuit of the hardware.
  • section 1 1 shows the Blood oxygen sensor (Sp02) protection cap in closed condition
  • section 12 showing the electrodes used for Electrocardiograph measurement
  • section 13 showing opening for the USB connector
  • section 16 and 17 showing the indicator led for power and wireless connection respectively
  • section 14 and 15 showing the switch for turning on the power and wireless circuit of the hardware.
  • FIG. 5 shows the block diagram of the hardware section of the invention including the signal processing and conditioning sections, microcontroller, wireless, and battery charging sections.
  • Figure 6 shows yet another design for the optical sensor.
  • the two pictures in the Figure 6 shows two different positions of the same device.
  • the upper side of the chamber is lifted on the right side picture which is evident from the gap (section 10) due to the spring loaded sliding part (6) moving up to accommodate multiple finger sizes.
  • Sections 4 and 9 both show the same single spring assembly that makes sure that the sliding part 6 is loaded appropriately so that the fingers are compactly positioned during the usage.
  • Sections 7 and 8 are parts of the chamber for measuring Sp02.
  • Section 5 is the cap of spo2 sensor in open condition.
  • Figure 7 shows yet another perspective figure of the invention with section 18 highlighting the Infrared sensor assembly for temperature measurement and section 19 showing the ECG electrodes.
  • Figure 8 shows the device (34) attached to a human using multipurpose pouch/band (35) to be used during exercise detection.
  • Section 24 of figure 8 shows the device and the supporting pouch/band(25) worn by the user around his/her arm.
  • Figure 9 section 33 shows the same band/pouch worn by the user around the lower leg with the device 32.
  • Figure 10 section 26 shows the device in normal operation where user places the invention in the pocket and the invention keeps monitoring the user's activities.
  • Figure 1 1 shows the cross-section assembly of the Infrared temperature sensor in the invention where section 30 shows metal cap which acts as heat sink for the infrared sensor(27). The figure also shows the positions of the ECG Electrodes (37) and the Sp02 cap (38) in closed condition.
  • Figure 12 shows same design but with support for using a metal electrode (31 ) in place of using a Circular AgCI electrode. It also shows the Sp02 cap(32) in closed condition.
  • Figure 13 shows the block diagram of the invention communicating with the external mobile/desktop computing device which acts as the display, input, algorithm processing and database section along with the ability to upload to a cloud or external storage server via internet/intranet.
  • Figure 14 shows the block diagram of the ECG algorithm running in the microprocessor which is used to detect the peaks and heart rate variability.
  • Figure 15 shows the result of the operation on an actual signal taken from a laboratory condition.
  • Figure 16 shows the relation between the electrocardiogram signal's peak and pulse wave signal peak called as Pulse Wave Transit time.
  • Figure 17 shows the output signal from the visual and infrared probes for Pulse wave detection and the filtered output of these two input signals.
  • Figure 18 shows the Heart Rate Variability with section 79 showing an example of less variability and section 78 showing an example of high heart rate variability.
  • Figure 19 shows the algorithm of the step and activity counter block of the current invention with the sections 80, 81 and 82 showing the different sensor data collected, section 83 shows the features extracted from the sensors and section 84 and 85 shows the output from the machine learning model.
  • Figure 20 shows the graph of the polynomial regression on the pulse transit time value to get the relation to the Systole and Diastole values calculate the Blood pressure.
  • FIG. 21 section 87 and 88 shows the ECG and Accelerometer data collected, section 89 shows the different features extracted from the image and section 90 and 91 shows the result of the prediction model identifying the heart conditions.
  • Figure 22 shows the block diagram of the data sharing feature which is allowed by the invention with the ability of the user to dynamically select the type of data which could be shared.
  • Figure 23 shows the example different trends monitoring with the data captured from the invention.
  • the invention includes an electrocardiogram, pulse wave detection optical visual and infrared probes, Infrared temperature sensor , 3-Axis accelerometer, Gyroscope and Magnetometer using which Heart Rate Variability analysis, Blood Oxygen, Temperature, Stress, Respiration and activity level is detected .
  • the result from the electrocardiogram and the pulse wave detected by the visual and infrared probes is correlated to calculate the Blood pressure of the individual.
  • Another exemplary embodiment is that the user's physical activity is tracked in terms of number of type of activity performed by the user like walking , running, types of exercises performed is tracked along with the other health parameters and sent to the mobile computing device for data logging and analysis.
  • the user's heart rate variability analysis is calculated with both resting and active
  • the calculated health parameter data is stored in the Mobile/Desktop computing device and the data and trends of the user are analyzed over the course of the usage of the gadget with the ability to set goals to be achieved by the user and to track the health and fitness data.
  • FIG. 2 An example of the device in usage is given in Figure 2 where the user places two fingers on the metal electrode and one finger in the pulse oximeter section to measure the biological signals.
  • FIGS 8, 9 and 10 Yet another example of intended operation is given in figures 8, 9 and 10 where the invention could be worn on the arm, placed in the pocket or put as a leg monitor and based on the place where the inversion is put in the body different body movements could be monitored and controlled.
  • the invention is proposed to be used in Home and Hospital healthcare

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Abstract

L'invention concerne un procédé de mesure d'une combinaison de paramètres vitaux du corps humain comprenant l'ECG, le taux d'oxygène dans le sang, une mesure de tension artérielle sans brassard, la température, les données d'un moniteur d'activité comprenant un podomètre et un moniteur de sommeil. Les données calculées sont transmises à distance par l'intermédiaire d'une interface sans fil à un dispositif informatique mobile ou de bureau et le résultat final est également téléchargé vers un serveur central pour d'autres analyses de données.
PCT/IN2017/050280 2017-05-19 2017-07-08 Moniteur d'activité physique et de paramètres vitaux portable sans fil WO2018211519A1 (fr)

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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111603151A (zh) * 2020-06-17 2020-09-01 中国医学科学院生物医学工程研究所 一种基于时频联合分析的无创血液成分检测方法及系统
CN111728597A (zh) * 2020-08-26 2020-10-02 四川科道芯国智能技术股份有限公司 健康监测设备和健康监测手表
CN111839494A (zh) * 2020-09-04 2020-10-30 广东电网有限责任公司电力科学研究院 一种心率监测方法及系统

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Publication number Priority date Publication date Assignee Title
EP1724684A1 (fr) * 2005-05-17 2006-11-22 BUSI Incubateur d'entreprises d'AUVEFGNE Système et méthode pour planning de séquence de tâches, analyse de signaux et capteur à distance
TW201208646A (en) * 2010-07-28 2012-03-01 Flore Ingo Portable diagnostic measuring device
IN2014CH01516A (fr) * 2014-03-21 2015-09-25 American Megatrends India Private Ltd

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP1724684A1 (fr) * 2005-05-17 2006-11-22 BUSI Incubateur d'entreprises d'AUVEFGNE Système et méthode pour planning de séquence de tâches, analyse de signaux et capteur à distance
TW201208646A (en) * 2010-07-28 2012-03-01 Flore Ingo Portable diagnostic measuring device
IN2014CH01516A (fr) * 2014-03-21 2015-09-25 American Megatrends India Private Ltd

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111603151A (zh) * 2020-06-17 2020-09-01 中国医学科学院生物医学工程研究所 一种基于时频联合分析的无创血液成分检测方法及系统
CN111603151B (zh) * 2020-06-17 2023-05-16 深圳智领人工智能健康科技有限公司 一种基于时频联合分析的无创血液成分检测方法及系统
CN111728597A (zh) * 2020-08-26 2020-10-02 四川科道芯国智能技术股份有限公司 健康监测设备和健康监测手表
CN111839494A (zh) * 2020-09-04 2020-10-30 广东电网有限责任公司电力科学研究院 一种心率监测方法及系统

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