[go: up one dir, main page]

WO2025144405A1 - Étalonnage d'impédance corporelle basé sur la température corporelle pour estimation et détermination biométriques - Google Patents

Étalonnage d'impédance corporelle basé sur la température corporelle pour estimation et détermination biométriques Download PDF

Info

Publication number
WO2025144405A1
WO2025144405A1 PCT/US2023/086227 US2023086227W WO2025144405A1 WO 2025144405 A1 WO2025144405 A1 WO 2025144405A1 US 2023086227 W US2023086227 W US 2023086227W WO 2025144405 A1 WO2025144405 A1 WO 2025144405A1
Authority
WO
WIPO (PCT)
Prior art keywords
user
data
impedance
temperature
biometric
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
PCT/US2023/086227
Other languages
English (en)
Inventor
Peter Winthrop RICHARDS
Jaclyn Leverett Wasson
Aniket Sanjay Deshpande
Seobin Jung
Alexandros Antonios PANTELOPOULOS
Mingwu Gao
Keerthana NATARAJAN
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Google LLC
Original Assignee
Google LLC
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Google LLC filed Critical Google LLC
Priority to PCT/US2023/086227 priority Critical patent/WO2025144405A1/fr
Publication of WO2025144405A1 publication Critical patent/WO2025144405A1/fr
Pending legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • 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
    • A61B5/05—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves
    • A61B5/053—Measuring electrical impedance or conductance of a portion of the body
    • A61B5/0537—Measuring body composition by impedance, e.g. tissue hydration or fat content
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/45—For evaluating or diagnosing the musculoskeletal system or teeth
    • A61B5/4504—Bones
    • A61B5/4509—Bone density determination
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/48—Other medical applications
    • A61B5/4866—Evaluating metabolism
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/48—Other medical applications
    • A61B5/4869—Determining body composition
    • A61B5/4872—Body fat
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/48—Other medical applications
    • A61B5/4869—Determining body composition
    • A61B5/4875—Hydration status, fluid retention of the body
    • 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
    • A61B5/681—Wristwatch-type devices
    • 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
    • A61B5/6823—Trunk, e.g., chest, back, abdomen, hip
    • 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
    • A61B2560/00—Constructional details of operational features of apparatus; Accessories for medical measuring apparatus
    • A61B2560/02—Operational features
    • A61B2560/0223—Operational features of calibration, e.g. protocols for calibrating sensors
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B2560/00—Constructional details of operational features of apparatus; Accessories for medical measuring apparatus
    • A61B2560/02—Operational features
    • A61B2560/0242—Operational features adapted to measure environmental factors, e.g. temperature, pollution
    • A61B2560/0247—Operational features adapted to measure environmental factors, e.g. temperature, pollution for compensation or correction of the measured physiological value
    • A61B2560/0252—Operational features adapted to measure environmental factors, e.g. temperature, pollution for compensation or correction of the measured physiological value using ambient temperature
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B2562/00—Details of sensors; Constructional details of sensor housings or probes; Accessories for sensors
    • A61B2562/02—Details of sensors specially adapted for in-vivo measurements
    • A61B2562/0219—Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
    • 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
    • A61B5/68—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
    • A61B5/6825—Hand
    • 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
    • A61B5/6825—Hand
    • A61B5/6826—Finger
    • 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
    • A61B5/6829—Foot or ankle
    • 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

  • Example aspects of the present disclosure relate generally to determining biometrics of a user of a wearable computing device.
  • a wearable computing device can be worn, for instance, on a user’s wrist.
  • the wearable computing device can include a plurality of sensors such as, e.g., biometric sensors. In this manner, the wearable computing device can determine one or more biometrics of the user wearing the wearable computing device based at least in part on the data output by the biometric sensors.
  • a computer-implemented method for biometric monitoring includes obtaining, by a computing system comprising one or more computing devices, impedance sensor data from an impedance sensor of a computing device of the computing system, the impedance sensor data indicative of a body impedance of a user of the computing device.
  • the method further includes obtaining, by the computing system, temperature data associated with the user from one or more temperature sensors of the computing system, the temperature data comprising body temperature data indicative of a body temperature of the user.
  • the method further includes determining, by the computing system, a calibrated body impedance of the user based at least in part on the impedance sensor data and the temperature data.
  • the method further includes determining, by the computing system, a body composition biometric of the user based at least in part on the calibrated body impedance of the user and user data indicative of one or more characteristics of the user.
  • the body composition biometric of the user includes one of a body fat percentage of the user, a fat-free mass biometric of the user, a bone mineral content of the user, a basal metabolic rate of the user, or a total body water biometric of the user.
  • the body temperature data includes at least one of skin temperature data indicative of a skin temperature of the user, tissue temperature data indicative of a temperature of subcutaneous tissue of the user, muscle temperature data indicative of a temperature of muscle tissue of the user, or core temperature data indicative of a core temperature of the user.
  • the user data includes demographic data of the user and anthropometric data of the user.
  • determining the body composition biometric of the user includes providing, by the computing system, the calibrated body impedance of the user and the user data to a biometric estimation model of the computing system and determining, by the computing system, the body composition biometric of the user based at least in part on an output of the biometric estimation model.
  • determining the body composition biometric of the user includes providing, by the computing system, the user data to the biometric estimation model and determining, by the computing system, the body composition biometric of the user based at least in part an output of the biometric estimation model.
  • the method further includes monitoring, by the computing system, a relative change in the body impedance of the user with respect to the body temperature data during an observation period comprising a plurality of sampling periods.
  • the computing system is configured provide the relative change as training data to a biometric estimation model of the computing system, which is configured to determine the body composition biometric of the user based at least in part on the calibrated body impedance of the user and the user data indicative of the one or more characteristics of the user.
  • monitoring the relative change in the body impedance of the user includes: obtaining, by the computing system, distal sensor data from the impedance sensor during each of the plurality of sampling periods of the observation period when the impedance sensor is contacting the user at a distal location of the user; obtaining, by the computing system, the body temperature data from the one or more temperature sensors during each of the plurality of sampling periods of the observation period; and determining, by the computing system, the relative change in the distal sensor data during the observation period based at least in part on the body temperature data obtained during each of the plurality of sampling periods of the observation period.
  • the distal location of the user includes a hand of the user, a wrist of the user, a finger of the user, a foot of the user, or an ankle of the user.
  • the relative change in the distal sensor data demonstrates a pseudo-linear relationship with the body temperature of the user.
  • monitoring the relative change in the body impedance of the user includes: obtaining, by the computing system, proximal sensor data from the impedance sensor during each of the plurality of sampling periods of the observation period, the impedance sensor contacting the user at a proximal location of the user; obtaining, by the computing system, the body temperature data from the one or more temperature sensors during each of the plurality of sampling periods of the observation period; and determining, by the computing system, the relative change in the proximal sensor data during the observation period based at least in part on the body temperature data obtained during each of the plurality of sampling periods of the observation period.
  • the proximal location of the user is a trunk of the user.
  • a wearable computing device in another aspect, includes a housing, a base plate coupled to the housing that defines a bottom surface of the housing, one or more biometric sensors positioned on the bottom surface of the housing that are configured to obtain biometric data of a user wearing the wearable computing device, and one or more processors.
  • the one or more processors are configured to determine a body impedance of the user based at least in part on impedance data received from the one or more biometric sensors, determine a body temperature of the user based at least in part on body temperature data received from the one or more biometric sensors, calibrate the impedance data based at least in part on the body temperature data to determine a calibrated body impedance of the user, and determine a body composition biometric of the user based at least in part on the calibrated body impedance of the user and user data indicative of one or more characteristics of the user.
  • the wearable computing device further includes an internal temperature sensor disposed within the housing and an ambient temperature sensor on or within the housing.
  • the internal temperature sensor is configured to obtain internal temperature data indicative of an internal temperature of the wearable computing device
  • the ambient temperature sensor is configured to obtain ambient temperature data indicative of an ambient temperature of an environment of the user
  • the one or more processors are further configured to calibrate the impedance data based at least in part on the bodytemperature data, the internal temperature data, and the ambient temperature data.
  • the body composition biometric of the user includes at least one of a body fat percentage of the user or a fat-free mass biometric of the user.
  • the user data includes demographic data of the user and anthropometric data of the user.
  • a computing system in another aspect, includes an impedance sensor, one or more temperature sensors, and one or more processors.
  • the computing system further includes one or more computer-readable media that collectively store instructions that, when executed by the one or more processors, cause the computing system to perform operations.
  • the operations include: obtaining impedance sensor data from the impedance sensor, the impedance sensor data indicative of a body 7 impedance of a user of the computing system; obtaining temperature data associated with the user from the one or more temperature sensors, the temperature data comprising bodytemperature data indicative of a body temperature of the user: determining a calibrated body impedance of the user based at least in part on the impedance sensor data and the temperature data; and determining a body composition biometric of the user based at least in part on the calibrated body impedance of the user and user data indicative of one or more characteristics of the user.
  • FIG. 4 depicts a block diagram of components of a wearable computing device according to example embodiments of the present disclosure
  • FIGS. A-5B depict block diagrams of example models for determining a body composition biometric according to example embodiments of the present disclosure
  • FIG. 6 depicts a flow chart diagram of an example method according to example embodiments of the present disclosure
  • FIG. 8 depicts a computing system according to example embodiments of the present disclosure.
  • Example aspects of the present disclosure relate to computer systems and methods for determining and monitoring a body composition biometric of a user.
  • a computing system may include one or more computing devices, such as a wearable computing device, a mobile computing device (e.g., smartphone, tablet, etc.), and the like.
  • a computing device of the computing system may include one or more biometric sensors configured to obtain biometric data of a user of the computing device.
  • a wearable computing device may include an impedance sensor configured to obtain data indicative of a body impedance of the user wearing the wearable computing device. It should be understood that the discussions relating to a wearable computing device are for purposes of illustration and discussion.
  • body composition biometrics e.g.. body fat percentage (BF%), fat-free mass (FFM), bone mineral content (BMC), basal metabolic rate (BMR), total body water (TBW), etc.
  • body composition biometrics may be estimated based on a variety of data associated with the user.
  • body composition biometrics may be determined based on the user’s demographic information (e.g., age, sex, etc.), the user's anthropometric data (e.g., height, weight, waist circumference, etc.), and the user’s body impedance.
  • the data associated with the user that is used by the computing system to determine the user’s body composition namely, the body impedance data — must likewise be accurate.
  • a user’s body impedance may be determined based on measurements from an impedance sensor (e.g., an electrode) that contacts the user’s skin.
  • an impedance sensor e.g., an electrode
  • various confounders such as measurement posture and/or body conditions (e g., skin conditions) of the user, can affect the body impedance measurements by the impedance sensor, despite the user’s body composition remaining the same.
  • a user’s body impedance may vary based on, e.g., the angle of the measurement by the impedance sensor and/or the user’s body temperature.
  • a user’s body impedance may be used by the computing system to determine the user’s body composition
  • inaccuracies and inconsistencies in the measured body impedance data may result in inaccurate body composition estimations.
  • a user’s body temperature may include a variety 7 of temperatures and temperature measurements.
  • a user’s body temperature may include a temperature of the user’s skin (e.g., ‘"skin temperature”), a temperature of the user’s subcutaneous tissue (e.g., ‘'tissue temperature” and/or “internal temperature”), a temperature of the user's muscle(s) (e.g., “muscle temperature”), a temperature of the user’s core (e.g., “core temperature”), and the like.
  • skin e.g., ‘"skin temperature”
  • subcutaneous tissue e.g., ‘'tissue temperature” and/or “internal temperature”
  • a temperature of the user's muscle(s) e.g., “muscle temperature”
  • a temperature of the user’s core e.g., “core temperature”
  • Some devices such as wearable computing devices and/or non-wearable computing devices, provide body composition estimations in a similar manner as set forth above. That is, some devices provide body composition estimations based on demographic information, anthropometric information, and body impedance data measured by electrodes contacting skin. Hence, some devices are susceptible to providing inaccurate body composition estimations due, in large part, to the confounders discussed above.
  • Example aspects of the present disclosure are directed to computing systems and methods that reduce the adverse effects on body impedance measurements and body composition estimations caused by the confounders discussed above, thereby providing for more accurate and reliable body composition estimations.
  • example aspects of the present disclosure provide a computing system operable to measure the confounders (e.g., body temperature) and, subsequently, calibrate the measured body impedance data based on the measured confounders.
  • the confounders e.g., body temperature
  • computing systems according to examples of the present disclosure provide more accurate body composition estimations, because the body impedance data used in the body composition estimations is calibrated to reduce the adverse effects caused by the above-described confounders.
  • the temperature of a user’s skin and/or tissue under the skin are known to vary due to various factors, such as the environment the user is in, the clothing the user is wearing, and/or physiological states of the user. Temperature, however, is not easily controlled, and limiting body composition estimations to situations where the user is in a temperature-controlled environment is not practical, especially given the increase in popularity of consumer health devices (e.g., wearable computing devices).
  • example aspects of the present disclosure are directed to a computing system operable to obtain impedance data indicative of a body impedance of a user and temperature data associated with the user.
  • the computing system may be further operable to calibrate the impedance data based at least in part on the temperature data, thereby generating calibrated body impedance data.
  • the computing system may then use the calibrated body impedance data to determine the body composition of the user.
  • the computing system may be configured to calibrate (e.g., adjust) the impedance data to take into account the temperature-based confounders that typically adversely affect the body impedance data and body composition estimations, thereby- improving the accuracy of the body impedance data and the body composition estimations.
  • the determined body composition biometric may, for instance, be used for at least one of the following: displaying biometric characteristics of the user for informing the user about its health and/or fitness level; outputting at least one (e.g., audible, visual, haptic, etc.) alarm and/or notification in the computing system, e.g., at the wearable computing device and/or the mobile computing device; and/or triggering performing a software and/or hardware controlled operation in the computing system, e.g., at the wearable computing device and/or the mobile computing device, such as initiating a call or sending an electronic message (e.g., in case it is detected, based on the determined body composition biometric, that the user experiences and/or experienced a health-related infirmity, such as a cardiovascular disease).
  • a software and/or hardware controlled operation e.g., at the wearable computing device and/or the mobile computing device, such as initiating a call or sending an electronic message (e.g., in case it is detected, based on the
  • the computing system may obtain impedance sensor data from an impedance sensor of the computing system that is indicative of a body impedance of a user.
  • the computing system may obtain the impedance sensor data from an impedance sensor of a computing device of the computing system, such as an impedance sensor on a wearable computing device that is being worn by the user.
  • the computing system may also obtain temperature data associated with the user from one or more temperature sensors of the computing system.
  • the temperature data may include, for instance, body temperate data indicative of a body temperature of the user, such as skin temperature data, tissue temperature data, muscle temperature data, core temperature data, and the like.
  • the temperature data may further include device temperature data and ambient temperature data.
  • the computing system may obtain the device temperature data from a temperature sensor of the wearable computing device that is configured to measure an internal temperature of the wearable computing device.
  • the ambient temperature data may be indicative of a temperature of an environment in which the user and/or wearable computing device is located. In some embodiments, the ambient temperature data may be determined based on athermal model associated with one or more computing devices of the computing system.
  • the impedance sensor data and the temperature data may be obtained from any of the computing devices of the computing system without deviating from the scope of the present disclosure.
  • the impedance sensor data, body temperature data, and the device temperature data may be obtained from the wearable computing device, while the ambient temperature data may be obtained from another computing device, such as a mobile computing device (e.g.. smartphone, tablet) of the computing system.
  • the impedance sensor data, the body temperature data, the device temperature data, and the ambient temperature data may be obtained from another computing device, such as a mobile computing device of the computing system.
  • the computing system may be configured to determine a calibrated body impedance of the user based at least in part on the impedance sensor data and the temperature data. The computing system may then determine a body composition biometric of the user based at least in part on the calibrated body impedance of the user and other user-related data indicative of one or more characteristics of the user, such as demographic data and/or anthropometric data of the user.
  • the computing system may provide the impedance sensor data and the temperature data to one or more machine-learned models of the computing system, such as a bioimpedance calibration model.
  • the bioimpedance calibration model may be configured to calibrate the impedance sensor based at least in part on the temperature data.
  • the computing system may then determine the calibrated body impedance of the user based at least in part on an output of the bioimpedance calibration model. More particularly, subsequent to determining the calibrated body impedance of the user, the computing system may provide the calibrated body impedance of the user to another machine-learned model of the computing system, such as a biometric estimation model.
  • the computing system may also provide the other user-related data (e.g., demographic data, anthropometric data) to the biometric estimation model. Then, based on an output of the biometric estimation model, the computing system may determine the body composition biometric of the user.
  • the biometric estimation model may be configured to determine the calibrated body impedance of the user itself.
  • the computing system may bypass the bioimpedance calibration model and provide the impedance sensor data and the temperature sensor data directly to the biometric estimation model.
  • the computing system may also provide the other user-related data directly to the biometric estimation model.
  • the computing system may determine the body composition biometric of the user based at least in part on an output of the biometric estimation model.
  • the computing system may be configured to monitor a relative change in the body impedance of the user with respect to the body temperature data over the course of an observation period.
  • the observation period may include a plurality of sampling periods.
  • the computing system may also provide data corresponding to the monitored relative change to the one or more machine-learned models (e.g., bioimpedance calibration model, biometric estimation model) as training data to further refine the associated outputs and, hence, ensure the accuracy of those outputs.
  • machine-learned models e.g., bioimpedance calibration model, biometric estimation model
  • the computing system may obtain body temperature data and impedance sensor data during the observation period.
  • the body temperature data and the impedance sensor data may be provided by one or more biometric sensors (e.g., impedance sensor measuring impedance, temperature sensor measuring temperature) at a point of contact with the user’s skin.
  • the body temperature data and the impedance sensor data may be obtained from the same point of contact with the user’s skin; in other embodiments, the body temperature data and the impedance sensor data may be obtained at different points of contact with the user’s skin.
  • the impedance sensor data may be measured by an impedance sensor that contacts the user at a proximal location (e.g., inner side of knee, inner side of elbow) of the user.
  • the relative change of the impedance sensor data may demonstrate a substantially flat (e.g., unchanging) relationship with respect to the skin temperature of the user.
  • proximal location refers to a location on the user that is proximate to the user’s trunk, such as the user’s torso, an inner side of the user’s knee, an inner side of the user’s elbow, and the like.
  • proximal sensor data refers to impedance sensor data measured from a proximal location of the user.
  • Example aspects of the present disclosure provide numerous technical effects and benefits. For instance, example aspects of the present disclosure provide a computing system operable to mitigate error in bioimpedance and body composition measurements. The error in bioimpedance and body composition measurements is reduced by obtaining temperature data associated with the user and using that temperature data to calibrate the impedance sensor signals. In this manner, computing systems of the present disclosure provide more accurate and more reliable bioimpedance and body composition measurements, which is an improvement over conventional commercial devices that base bioimpedance and body composition measurements on raw impedance data and fail to take confounders (e.g., body temperature) into account.
  • confounders e.g., body temperature
  • the present disclosure also enables the refinement of data received from sensors within the computing system by combining and analyzing data from multiple sensors in a w ay that allows the user to observe more than just raw data, but also trends and events inferred from the data. In this way, the present disclosure can also obviate the need for additional sensors within the computing system, thereby expending minimal computing resources by, e g., saving device space and processor usage.
  • the present disclosure also allows for more accurate devices to monitor bioimpedance and body composition biometrics of users in ways that are more efficient, predictable, and useful.
  • the terms “first,” “second,” and “third” may be used interchangeably to distinguish one component from another and are not intended to signify location or importance of the individual components.
  • the terms “includes” and “including” are intended to be inclusive in a manner similar to the term “comprising.”
  • the term “or” is generally intended to be inclusive (e.g., “A or B” is intended to mean “A or B or both”).
  • the term “at least one of’ in the context of. e.g., “at least one of A. B, and C” refers to only A, only B, only C, or any combination of A, B, and C.
  • range limitations may be combined and/or interchanged.
  • such terms when used in the context of an angle or direction, such terms include within ten degrees greater or less than the stated angle or direction, e.g., “generally vertical” includes forming an angle of up to ten degrees in any direction, e g., clockwise or counterclockwise, with the vertical direction V.
  • the wearable computing device 100 can include a housing 110.
  • the housing 110 can include a base plate 112 coupled to the housing 110. In this manner, the base plate 112 can define a bottom surface of the housing 110.
  • the housing 110 can define a cavity (e.g., internal volume) (not shown) in which one or more electronic components (e.g., disposed on printed circuit boards) are disposed.
  • the wearable computing device 100 can include a printed circuit board (e.g., flexible printed circuit board) (not shown) disposed within the cavity.
  • one or more electronic components can be disposed on the printed circuit board.
  • the wearable computing device 100 can further include a battery’ (not shown) that is disposed within the cavity defined by the housing.
  • the wearable computing device 100 can also include one or more internal temperature sensors (not shown) within the cavity' that are configured to obtain internal temperature data indicative of an internal temperature of the w earable computing device.
  • the wearable computing device 100 can include a first band 120 coupled to the housing 110 at a first location and a second band 122 coupled to the housing 110 at a second location.
  • the first band 120 and the second band 122 can be coupled to one another at a third location (not show n) to secure the housing 110 to the arm 102 of the user.
  • the first band 120 can include a buckle or clasp (not shown).
  • the second band 122 can define a plurality of apertures 124 spaced apart from one another along a length of the second band 122.
  • a prong of the buckle associated with the first band 120 can extend through one of the plurality of openings defined by the second band 122 to couple the first band 120 to the second band 122.
  • first band 120 can be coupled to the second band 122 using any suitable type of fastener.
  • first band 120 and the second band 122 can include a magnet (not shown).
  • the first band 120 and the second band 122 can be magnetically coupled to one another to secure the housing 110 to the arm 102 of the user.
  • the wearable computing device 100 can include one or more processors 202.
  • the one or more processors 202 can include any suitable processing device (e.g., a processor core, a microprocessor, an application specific integrated circuit (AISC). a field programmable gate array (FPGA), a microcontroller, etc ).
  • the wearable computing device 100 can further include a memory 204.
  • the memory 204 can include one or more non- transitory computer-readable storage media, such as random access memory (RAM), readonly memory (ROM), electronically erasable programmable ready-only memory (EEPROM), erasable programmable read-only memory (EPROM), flash memory devices, and combinations thereof.
  • the memory 204 can store data 206 and instructions 208 that, when executed by the one or more processors 202, cause the one or more processors 202 to perform operations disclosed herein.
  • biometric data obtained from the one or more biometric sensors 216 of the wearable computing device 100 can indicate whether the wearable computing device 100 is currently being worn by the user. For instance, in some embodiments, the biometric data obtained from the one or more biometric sensors 216 can indicate the wearable computing device 100 is not being worn (e.g., off- wrist) by the user. In such embodiments, the one or more processors 202 can be configured to disable impedance data collection functionality while the biometric data obtained from the one or more biometric sensors 216 indicates the wearable computing device 100 is not being worn by the user. In this manner, erroneous data from the one or more biometric sensors 216 can be ignored.
  • the one or more processors 202 can be configured to enable impedance data collection functionality when the biometric data obtained from the one or more biometric sensors 216 indicates the wearable computing device 100 is being worn (e.g., on-wrist) by the user.
  • the wearable computing device 100 can include one or more machine-learned models 220.
  • the one or more machine-learned models 220 can be stored in the memory 204 of the wearable computing device 100.
  • the one or more machine-learned models 220 can be stored in the memory' of one or more devices that are remote relative to the wearable computing device 100.
  • the one or more machine-learned models 220 can be stored in memory' of the mobile computing device 610 (FIG. 8) that is communicatively coupled with the wearable computing device 100 via a network 620 (FIG. 8).
  • the one or more machine-learned models 220 can be stored on one or more servers (not shown) that are communicatively coupled with the wearable computing device 100 via the network 620.
  • the one or more machine-learned models 220 can be configured to calibrate impedance sensor data with temperature data associated with the user (e.g., body temperature, ambient temperature, device temperature) to determine a body composition biometric of the user (e.g., body fat percentage (BF%).
  • body fat percentage e.g., body fat percentage (BF%).
  • FFM fat-free mass
  • BMC bone mineral content
  • BMR basal metabolic rate
  • TW total body water
  • the one or more machine- learned models 220 may include a bioimpedance calibration model 310 and a biometric estimation model 320.
  • the bioimpedance calibration model 310 may be configured to calibrate impedance sensor data 302 measured by the one or more biometric sensors 216 (FIG. 4) based at least in part on temperature data measured by the one or more sensors 210, such as body temperature data 304, device temperature data 306, and ambient temperature data 308.
  • the ambient temperature data 308 may be directly measured by the one or more sensors 210.
  • temperature data may be obtained from one or more devices, and the ambient temperature data 308 may be determined based on the temperature data and a thermal model of the one or more measuring devices.
  • the bioimpedance calibration model 310 may output data indicative of a calibrated body impedance of the user (hereinafter referred to as “calibrated body impedance data 312”). It should be understood that the temperature data may also be measured by one or more temperature sensors of a computing device remote to the mobile computing device 100, such as mobile computing device 610 (FIG. 8) and/or remote computing system 630 (FIG. 8). The calibrated body impedance data 312 may then be provided to the biometric estimation model 320.
  • the biometric estimation model 320 may be configured to determine a body composition biometric 330 of the user based at least in part on the calibrated body impedance data 312.
  • user data associated with one or more characteristics of the user may also be provided to the biometric estimation model 320.
  • the biometric estimation model 320 may, in turn, generate data indicative of the body composition 330 of the user.
  • the demographic data 322 and the anthropometric data 324 may be stored in the memory 204 (FIG. 4) of the wearable computing device. Additionally and/or alternatively, in other embodiments, the demographic data 322 and the anthropometric data 324 may be stored in the memory of a computing device remote to the user computing device 100, such as mobile computing device 610 (FIG. 8) and/or remote computing system 630 (FIG. 8).
  • the biometric estimation model 320 may be configured to calibrate the impedance sensor data 302.
  • the impedance sensor data 302 and the temperature data e.g., body temperature data 304, device temperature data 306, ambient temperature data 308 may be provided directly to the biometric estimation model 320.
  • user data associated with one or more characteristics of the user e.g., demographic data 322 and anthropometric data 324) may also be provided to the biometric estimation model 320.
  • the biometric estimation model 320 may, in turn, generate data indicative of the body composition 330 of the user.
  • FIG. 6 a flow diagram of a computer-implemented method 400 for biometric monitoring of a user of a computing device (e.g., wearable computing device, mobile computing device, tablet computing device, etc.) is provided according to example embodiments of the present disclosure.
  • the method 400 may be implemented using, for instance, the wearable computing device 100 discussed above with reference to FIGS. 1-5B.
  • the method 400 may be implemented by a computing device (e.g., server, smartphone, etc.) that is communicatively coupled to the wearable computing device 100.
  • FIG. 6 depicts steps performed in a particular order for purposes of illustration and discussion. Those of ordinary' skill in the art, using the disclosures provided herein, will understand that various steps of any of the methods described herein can be omitted, expanded, performed simultaneously, rearranged, and/or modified in various ways without deviating from the scope of the present disclosure. Furthermore, various steps (not illustrated) can be performed without deviating from the scope of the present disclosure.
  • the method 400 is generally discussed with reference to the wearable computing device described above with reference to FIGS. 1-5B, the plurality of sensors 210 described above with reference to FIGS. 4-5B, and the machine- learned models 220 described above with reference to FIGS. 4-5B. However, it should be understood that aspects of the present method 400 can find application with any suitable wearable computing device, sensor, and/or machine-learned model.
  • the method 400 may include, at (402), obtaining, by a computing system comprising one or more computing devices, impedance sensor data from an impedance sensor of a computing device of the computing system. More particularly, a computing system, such as computing system 600 discussed below with reference to FIG. 8. may obtain impedance sensor data indicative of a body impedance of a user. In some embodiments, the impedance sensor data may be obtained by the one or more biometric sensors 216 of a computing device, such as the wearable computing device 100. Additionally and/or alternatively, the impedance sensor data may be obtained from a biometric sensor of another computing device in the computing system 600. such as mobile computing device 610.
  • the method 400 may include, at (404), obtaining, by the computing system, temperature data associated with the user from one or more temperature sensors of the computing system. More particularly, the computing system 600 may obtain temperature data associated with the user, such as body temperature data 304, device temperature data 306, and/or ambient temperature data 308. In some embodiments, the temperature data may be obtained from the one or more sensors 210 of a computing device, such as the wearable computing device 100. Additionally and/or alternatively, the temperature data may be obtained from one or more temperature sensors of another computing device in the computing system 600, such as mobile computing device 610.
  • a portion of the temperature data may be obtained from the one or more sensors 210 of the wearable computing device 100, and another portion of the temperature data may be obtained from the one or more temperature sensors of another computing device in the computing system 600, such as mobile computing device 610.
  • the body temperature data 304 may include, for instance, skin temperature data indicative of a skin temperature of the user, tissue temperature data indicative of a temperature of subcutaneous tissue of the user, muscle temperature data indicative of a temperature of muscle tissue of the user, core temperature data indicative of a core temperature of the user, and the like.
  • the method 400 may include, at (406), determining, by the computing system, a calibrated body impedance of the user based at least in part on the impedance sensor data and the temperature data.
  • the method 400 may include calibrating a body impedance of the user based at least in part on the impedance sensor data and the temperature data to determine a calibrated body impedance of the user.
  • the method 400 may include using the bioimpedance calibration model to calibrate the impedance sensor data based at least in part on the temperature data.
  • the computing system 600 may provide the impedance sensor data 302 and the temperature data (body temperature data 304, device temperature data 306, ambient temperature data 308) to a bioimpedance calibration model 310 of the computing system 600, which may be used to calibrate the impedance sensor data 302 based on the temperature data (e.g., body temperature data 304, device temperature data 306, ambient temperature data 308).
  • the computing system 600 may then determine the calibrated body impedance based at least in part on calibrated impedance data 312 output by the bioimpedance calibration model 310.
  • the computing system 600 may perform a calibration for the body impedance of the user based at least in part on the impedance sensor data 302 and the temperature data (e.g., body temperature data 304, device temperature data 306, ambient temperature data 308) to determine the calibrated body impedance (e.g.. calibrated body impedance data 312).
  • the temperature data e.g., body temperature data 304, device temperature data 306, ambient temperature data 308
  • the calibrated body impedance e.g. calibrated body impedance data 312
  • the method 400 may include, at (408), determining, by a biometric estimation model of the computing system, a body composition biometric of the user based at least in part on the calibrated body impedance of the user and user data indicative of one or more characteristics of the user. More particularly, in some embodiments, the computing system 600 may provide the calibrated impedance data 312 output by the bioimpedance calibration model 310 to the biometric estimation model 320. The computing system 600 may also provide the user data indicative of one or more characteristics of the user to the biometric estimation model 320. More particularly, as noted above the user data indicative of one or more characteristics of the user may include the demographic data 322 and the anthropometric data 324. The computing system 600 may then determine the body composition biometric of the user based at least in part on the data indicative of a body composition 330 of the user output by the biometric estimation model 320.
  • the method 400 may include using the bioimpedance estimation model to calibrate the impedance sensor data based at least in part on the temperature data.
  • the computing system 600 may provide the impedance sensor data 302 and the temperature data (body temperature data 304, device temperature data 306. ambient temperature data 308) to a biometric estimation model 320 of the computing system 600.
  • the biometric estimation model 320 may be configured to calibrate the impedance sensor data 302 based at least in part on the temperature data (body temperature data 304, device temperature data 306, ambient temperature data 308).
  • the computing system 600 may also provide the user data (e.g...
  • the computing system 600 may then determine the body composition biometric of the user based at least in part on the data indicative of a body composition 330 of the user output by the biometric estimation model 320.
  • the determined body composition biometric may be used for at least one of the following: displaying biometric characteristics of the user for informing the user about its health and/or fitness level; outputting at least one (e.g., audible, visual, haptic, etc.) alarm and/or notification in the computing system, e.g., at the wearable computing device 100 and/or the mobile computing device 610 and/or the remote computing system 630; and/or triggering performing a software and/or hardware controlled operation in the computing system, e.g., at the wearable computing device 100 and/or the mobile computing device 610 and/or the remote computing system 630, such as initiating a call or sending an electronic message (e.g., in case it is detected, based on the determined body composition biometric, that the user experiences and/or experienced a health-related infirmity, such as a cardiovascular disease).
  • a software and/or hardware controlled operation e.g., at the wearable computing device 100 and/or the mobile computing device 610 and/or the remote computing
  • the method 400 may include, at (410), monitoring, by the computing system, a relative change in the body impedance of the user with respect to the body temperature data. More particularly, the computing system 600 may monitor a relative change in the impedance sensor data 302 with respect to the body temperature data 304 during an observation period comprising a plurality of sampling periods. Based on the relative change, the computing system 600 may provide training data to the biometric estimation model 320 and/or the bioimpedance calibration model 310.
  • the computing system 600 may be configured to obtain distal sensor data from the one or more biometric sensors 216 (e.g.. impedance sensor) during each of the plurality of sampling periods of the observation period.
  • the impedance sensor may be contacting the user at a distal location (e.g., wrist, hand, finger, ankle, foot, etc.) of the user.
  • the computing system 600 may obtain body temperature data 604 from the one or more biometric sensors 216 (e.g.. temperature sensor) during each of the plurality of sampling periods of the observation periods.
  • the computing system 600 may determine the relative change in the distal sensor data during the observation period based at least in part on the body temperature data 304 obtained during each of the plurality of sampling periods. It should be understood that the one or more biometric sensors 216 may sample the distal sensor data and the skin temperature data any number of times during each of the plurality of sampling periods without deviating from the scope of the present disclosure.
  • the relative change in the distal sensor data may demonstrate a pseudo-linear relationship with the body temperature of the user.
  • plot 500 depicts example data corresponding to a relative change in distal sensor data (depicted on the y-axis) with respect to the measured body temperature of the user (depicted on the x-axis) over an observ ation period. More particularly, plot 500 depicts example data corresponding to the relative change in distal sensor data sampled at five different frequencies, each with respect to the measured body temperature. As shown, the relative change in the distal sensor data at each sampling frequency demonstrates a pseudo-linear relationship with the skin temperature of the user over the observ ation period.
  • the computing system 600 may be configured to obtain proximal sensor data from the one or more biometric sensors 216 (e.g., impedance sensor) during each of the plurality of sampling periods of the observation period.
  • the impedance sensor may be contacting the user at a proximal location (e.g., torso, inner side of thigh, inner side of elbow) of the user.
  • the computing system 600 may obtain body temperature data 304 from the one or more biometric sensors 216 (e.g., temperature sensor) during each of the plurality of sampling periods of the observation periods.
  • the computing system 600 may determine the relative change in the proximal sensor data during the observation period based at least in part on the body temperature data 304 obtained during each of the plurality of sampling periods. It should be understood that the one or more biometric sensors 216 may sample the proximal sensor data and the body temperature data any number of times during each of the plurality of sampling periods without deviating from the scope of the present disclosure.
  • the relative change in the proximal sensor data may demonstrate a substantially flat relationship with the body temperature of the user.
  • plot 550 depicts example data corresponding to a relative change in proximal sensor data (depicted on the y-axis) with respect to the measured body temperature of the user (depicted on the x-axis) over an observation period. More particularly, plot 550 depicts example data corresponding to the relative change in proximal sensor data sampled at five different frequencies, each with respect to the measured body temperature. As shown, the relative change in the proximal sensor data at each sampling frequency demonstrates a substantially flat relationship with the body temperature of the user over the observation period.

Landscapes

  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Surgery (AREA)
  • Biophysics (AREA)
  • Pathology (AREA)
  • Engineering & Computer Science (AREA)
  • Biomedical Technology (AREA)
  • Heart & Thoracic Surgery (AREA)
  • Medical Informatics (AREA)
  • Molecular Biology (AREA)
  • Physics & Mathematics (AREA)
  • Animal Behavior & Ethology (AREA)
  • General Health & Medical Sciences (AREA)
  • Public Health (AREA)
  • Veterinary Medicine (AREA)
  • Obesity (AREA)
  • Dentistry (AREA)
  • Oral & Maxillofacial Surgery (AREA)
  • Orthopedic Medicine & Surgery (AREA)
  • Rheumatology (AREA)
  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
  • Radiology & Medical Imaging (AREA)
  • Measurement And Recording Of Electrical Phenomena And Electrical Characteristics Of The Living Body (AREA)

Abstract

L'invention concerne un procédé mis en œuvre par ordinateur pour la surveillance biométrique. Le procédé consiste à obtenir des données de capteur d'impédance provenant d'un capteur d'impédance qui indique une impédance corporelle d'un utilisateur. Le procédé consiste à obtenir des données de température associées à l'utilisateur provenant d'un ou de plusieurs capteurs de température, telles que des données de température corporelle indiquant une température corporelle de l'utilisateur. Le procédé consiste à déterminer une impédance corporelle étalonnée de l'utilisateur sur la base, au moins en partie, des données de capteur d'impédance et des données de température. Le procédé consiste à déterminer un indice biométrique de composition corporelle de l'utilisateur sur la base, au moins en partie, de l'impédance corporelle étalonnée de l'utilisateur et des données d'utilisateur indiquant une ou plusieurs caractéristiques de l'utilisateur. Le procédé consiste à surveiller un changement relatif de l'impédance corporelle de l'utilisateur par rapport aux données de température corporelle pendant une période d'observation ayant une pluralité de périodes d'échantillonnage.
PCT/US2023/086227 2023-12-28 2023-12-28 Étalonnage d'impédance corporelle basé sur la température corporelle pour estimation et détermination biométriques Pending WO2025144405A1 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
PCT/US2023/086227 WO2025144405A1 (fr) 2023-12-28 2023-12-28 Étalonnage d'impédance corporelle basé sur la température corporelle pour estimation et détermination biométriques

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/US2023/086227 WO2025144405A1 (fr) 2023-12-28 2023-12-28 Étalonnage d'impédance corporelle basé sur la température corporelle pour estimation et détermination biométriques

Publications (1)

Publication Number Publication Date
WO2025144405A1 true WO2025144405A1 (fr) 2025-07-03

Family

ID=89940889

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/US2023/086227 Pending WO2025144405A1 (fr) 2023-12-28 2023-12-28 Étalonnage d'impédance corporelle basé sur la température corporelle pour estimation et détermination biométriques

Country Status (1)

Country Link
WO (1) WO2025144405A1 (fr)

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20050070778A1 (en) * 2003-08-20 2005-03-31 Lackey Robert P. Hydration monitoring
US20160324440A1 (en) * 2014-01-06 2016-11-10 Samsung Electronics Co., Ltd. Method and apparatus for measuring body fat using mobile device
US20210290156A1 (en) * 2020-03-23 2021-09-23 Nix, Inc. Wearable systems, devices, and methods for measurement and analysis of body fluids
WO2023027685A1 (fr) * 2021-08-23 2023-03-02 Fitbit Llc Agencement d'électrodes d'activité électrodermique continues côté poignet sur un dispositif habitronique pour détecter des événements de stress

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20050070778A1 (en) * 2003-08-20 2005-03-31 Lackey Robert P. Hydration monitoring
US20160324440A1 (en) * 2014-01-06 2016-11-10 Samsung Electronics Co., Ltd. Method and apparatus for measuring body fat using mobile device
US20210290156A1 (en) * 2020-03-23 2021-09-23 Nix, Inc. Wearable systems, devices, and methods for measurement and analysis of body fluids
WO2023027685A1 (fr) * 2021-08-23 2023-03-02 Fitbit Llc Agencement d'électrodes d'activité électrodermique continues côté poignet sur un dispositif habitronique pour détecter des événements de stress

Similar Documents

Publication Publication Date Title
CN109310369B (zh) 用于估计血脉搏波特征的多传感器系统
CN109069004B (zh) 用于确定可穿戴设备在对象上的位置和取向中的至少一个的方法和装置
US11089992B2 (en) Fetal movement monitor
JP2023171910A (ja) 電子機器及びシステム
CN109893110B (zh) 一种校准动态血压的方法及装置
JP6020082B2 (ja) 生体信号計測装置、生体信号計測方法及び生体信号計測プログラム
US20060195020A1 (en) Methods, systems, and apparatus for measuring a pulse rate
US20190272916A1 (en) Synthetic simulation system for testing of physiological monitoring devices
US11350856B2 (en) Electronic device and estimation system
US10602935B2 (en) Information processing apparatus, method and storage medium
Rohit et al. Iot based health monitoring system using raspberry PI-review
JP2009519737A (ja) 人の心拍数及び／又は心拍数変動を監視する装置並びに同じ機能を含むリストウォッチ
TW201521681A (zh) 心臟監控系統
EP3578100A1 (fr) Procédé et appareil servant à estimer une tendance dans un substitut de pression sanguine
US20210369200A1 (en) Hydration measurement with a watch
CN107374600A (zh) 基于多生理参数的实时监测方法及系统
WO2007053146A1 (fr) Procedes, systemes et dispositif permettant de mesurer la frequence du pouls
CN110139600B (zh) 用户终端装置
JP6191284B2 (ja) 判定装置、方法及びプログラム
WO2025144405A1 (fr) Étalonnage d'impédance corporelle basé sur la température corporelle pour estimation et détermination biométriques
JP7367242B2 (ja) 電子機器、推定システム、推定方法及び推定プログラム
CN111700604A (zh) 检测腕表及其检测方法
JP7180216B2 (ja) 生体情報解析装置、生体情報解析方法、および生体情報解析システム
US20220015717A1 (en) Activity State Analysis Device, Activity State Analysis Method and Activity State Analysis System
Laureano et al. Respiratory rate meter with the Internet of things

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 23853718

Country of ref document: EP

Kind code of ref document: A1