EP4236776A1 - Détection mécano-acoustique avancée et applications de celle-ci - Google Patents

Détection mécano-acoustique avancée et applications de celle-ci

Info

Publication number
EP4236776A1
EP4236776A1 EP21887763.7A EP21887763A EP4236776A1 EP 4236776 A1 EP4236776 A1 EP 4236776A1 EP 21887763 A EP21887763 A EP 21887763A EP 4236776 A1 EP4236776 A1 EP 4236776A1
Authority
EP
European Patent Office
Prior art keywords
electronic device
sensor
imu
living subject
data
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
EP21887763.7A
Other languages
German (de)
English (en)
Inventor
Shuai Xu
Hyoyoung Jeong
Jong Yoon Lee
Kun Hyuck LEE
John A. Rogers
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.)
Northwestern University
Original Assignee
Northwestern University
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 Northwestern University filed Critical Northwestern University
Publication of EP4236776A1 publication Critical patent/EP4236776A1/fr
Pending legal-status Critical Current

Links

Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—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
    • A61B5/103—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
    • A61B5/1102—Ballistocardiography
    • 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
    • A61B5/113—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb occurring during breathing
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7203—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal
    • A61B5/7207—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal of noise induced by motion artifacts
    • A61B5/7214—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal of noise induced by motion artifacts using signal cancellation, e.g. based on input of two identical physiological sensors spaced apart, or based on two signals derived from the same sensor, for different optical wavelengths
    • 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
    • A61B5/08—Detecting, measuring or recording devices for evaluating the respiratory organs
    • A61B5/0823—Detecting or evaluating cough events
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/42—Detecting, measuring or recording for evaluating the gastrointestinal, the endocrine or the exocrine systems
    • A61B5/4205—Evaluating swallowing
    • 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
    • A61B5/4803—Speech analysis specially adapted for diagnostic purposes
    • 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
    • A61B5/6822—Neck
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
    • A61B5/6801—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
    • A61B5/683—Means for maintaining contact with the body
    • A61B5/6832—Means for maintaining contact with the body using adhesives
    • A61B5/6833—Adhesive patches

Definitions

  • Patent Application Serial No.16/970,023, filed August 14, 2020 which is a national stage entry of PCT Patent Application Serial No. PCT/US2019/018318, filed February 15, 2019, which itself claims priority to and the benefit of U.S. Provisional Patent Application Serial Nos.62/710,324, filed February 16, 2018, 62/631,692, filed February 17, 2018, and 62/753,203, filed October 31, 2018.
  • Each of the above-identified applications is incorporated herein by reference in its entirety.
  • FIELD OF THE INVENTION The present invention relates generally to biosensors, and more particularly to advanced mechano-acoustic sensing systems and applications of the same. BACKGROUND OF THE INVENTION
  • the background description provided herein is for the purpose of generally presenting the context of the invention.
  • Skin-interfaced devices for such purposes use precision, high-bandwidth accelerometers based on microelectromechanical system technologies in layouts that optimize sensitivity to motions of the surface of the skin across a broad range of frequencies, from nearly zero to several thousand hertz.
  • the resulting data reflect not only bulk motions of the body, as with conventional wearable devices, but also features from a broad range of body sounds, as with digital stethoscopes, but impervious to ambient sounds. Additional information appears in a range of frequencies between these limits.
  • the recordings when mounted on the neck or the chest, the recordings enable detailed assessments of cardiac activity from motions of the heart and from pulsatile flow of blood through near-surface arteries, of respiratory cycles from chest wall movements, of respiratory sounds from airflow through the lungs and trachea, of swallowing behaviors from laryngeal motions and actions of the esophagus, of vocalization patterns from vocal fold activation, and of movements and changes in orientation of the core body.
  • Distinct features in the temporal and spectral characteristics of these processes yield insights into physical activity and health status via a rich range of conventional (e.g., heart rate (HR)) and unconventional (e.g., coughing frequency) metrics, in a seamless manner, without privacy concerns that would follow from use of microphones or other recording devices.
  • HR heart rate
  • unconventional e.g., coughing frequency
  • This invention in certain aspects discloses a novel approach that overcomes the aforementioned limitations through advanced concepts in system designs and optimized choices in anatomical mounting locations, at the hardware level without the need for complex and often ineffective digital signal processing strategies.
  • the approach in some embodiments exploits a pair of time-synchronized, high-bandwidth accelerometers (inertial measurement units (IMUs)) at opposite ends of a skin-interfaced device that locates one of the IMUs at the suprasternal notch (SN) and the other at the sternal manubrium (SM).
  • IMUs intial measurement units
  • the invention relates to an electronic device for measuring physiological parameters of a living subject comprising at least a first IMU and a second IMU, the first IMU and the second IMU are time-synchronized to and spatially and mechanically separated from each other; and a microcontroller unit (MCU) electronically coupled to the first IMU and the second IMU for processing of data streams from the first IMU and the second IMU.
  • the first IMU is configured to measure data including a first signal related to a physiological signal of the living subject and a second signal
  • the second IMU is configured to measure data including at least the second signal.
  • the first signal measured by the first IMU has a signal strength greater than that the second signal measured by the first IMU.
  • the data measured by the first IMU and the second IMU are processed such that subtraction of the second signal measured by the second sensor from the second signal measured by the first sensor results in a stronger first signal that is a signal of interest.
  • the second signal is related to at least one of ambient, motion and vibration.
  • the data measured by the second IMU includes the first signal and the second signal.
  • a signal-to-noise ratio (SNR) of a signal measured by the first IMU and the second IMU together is lower than a first SNR of a signal measured by the first IMU individually, or a second SNR of a signal measured by the second IMU individually.
  • SNR signal-to-noise ratio
  • both of the first IMU and the second IMU are operably in mechanical communication with the skin of the living subject.
  • one of the first IMU and the second IMU is operably in directly mechanical communication with the skin of the living subject for sensing physiological signals of the body, while the other of the first IMU and the second IMU is operably in indirectly mechanical communication with the skin of the living subject.
  • the first IMU and the second IMU are operably in directly mechanical communication with the skin of the living subject.
  • one of the first IMU and the second IMU is separated from the rest of rigid components of the electronic device.
  • the electronic device also comprises at least first and second thermal sensing units, wherein one of the first and second thermal sensing units is thermally isolated from an ambient environment and configured to measure a body temperature of the living subject, and the other of the first and second thermal sensing units is configured to measure the ambient temperature.
  • each of the first and second thermal sensing units is embedded in a respective one of the first and second IMUs.
  • the electronic device is configured to measure a range of physiological information from activity of a cardiopulmonary system and movements of a core body to a diverse collection of processes across thoracic cavity, esophagus, pharynx, and oral cavity related to respiration, speech, swallowing, wheezing, coughing, and sneezing.
  • the electronic device is configured to separate signals associated with the cardiopulmonary system and related processes from those due to body movements.
  • the electronic device is configured to spatiotemporally map movements of the skin at this region of the anatomy onto which the electronic device is attached during cardiac and respiratory activities.
  • the electronic device is configured to continuously measure temperature, heart rate (HR), respiratory rate (RR), activity level, and body orientation, across a range of vigorous activities and conditions.
  • the electronic device is configured to monitor key symptoms of a patient with COVID-19 infection to track progress of recovery and response to therapies in hospital and/or home.
  • the electronic device is configured to measure any of respiratory or motion related digital biomarkers associated with coughing, swallowing, and/or specific motion related activities.
  • the electronic device is configured to assess coughing when the living subject is moving or immobile, and/or to measure muscle motion, when the living subject is moving.
  • the electronic device further comprises a bidirectional wireless communication system electronically coupled to the electronic device and configured to send an output signal from the electronic device to an external device.
  • the external device is a mobile device, a computer, or a cloud service.
  • the bidirectional wireless communication system is further configured to deliver commands from the external device to the electronic device.
  • the bidirectional wireless communication system comprises a controller that utilizes at least one of near field communication (NFC), Wi-Fi/Internet, Bluetooth, Bluetooth low energy (BLE), and cellular communication protocols for wireless communication.
  • the electronic device further comprises a customized app with a user interface deployed in the external device to allow a user to configure and operate the electronic device for data collection, data transfer, data storage and analysis, wireless charging, and monitoring of user’s conditions.
  • the customized app is configured to allow time-synchronized operation of a plurality of the electronic devices simultaneously.
  • the electronic device further comprises a power module coupled to the first IMU, the second IMU and the MCU for providing power thereto.
  • the power module comprises at least one battery for providing the power.
  • the battery is a rechargeable battery.
  • the power module further comprises a wireless charging module for wirelessly charging the rechargeable battery.
  • the power module further comprises a failure prevention element including a short-circuit protection component or a circuit to avoid battery malfunction.
  • the second IMU is placed in a manner that it bends and folds over the battery.
  • the electronic device further comprises a flexible printed circuit board (fPCB) having flexible and stretchable interconnects electrically connecting to electronic components including the first IMU, the second IMU and the MCU and the power module.
  • the electronic device further comprises an elastomeric encapsulation layer at least partially surrounding the electronic components and the flexible and stretchable interconnects to form a tissue-facing surface attached to the living subject and an environment- facing surface, wherein the tissue-facing surface is configured to conform to a skin surface of the living subject.
  • the encapsulation layer is formed of a flame retardant material.
  • the elastomeric encapsulation layer is a waterproof and biocompatible silicone enclosure.
  • the electronic device further comprises a biocompatible hydrogel adhesive for attaching the electronic device on the respective region of the living subject, wherein the biocompatible hydrogel adhesive is adapted such that signals from the living subject are operably conductible to the first IMU and the second IMU.
  • the electronic device is flexible and conformable to the skin with a specific geometrical polarity for mounting in an anatomical location of interest of the living subject.
  • the electronic device is a wearable, twistable stretchable, and/or bendable.
  • the invention in another aspect, relates to an electronic device for measuring physiological parameters of a living subject, comprising a sensor network comprising a plurality of sensor units operably deployed on a skin of the living subject, the plurality of sensor units being time-synchronized to and spatially and mechanically separated from each other; and an MCU electronically coupled to the plurality of sensor units for processing of data streams from the plurality of sensor units.
  • the plurality of sensor units are configured to measure a same physiological parameter, or different physiological parameters.
  • each of the plurality of sensor units comprises at least a first sensor and the second sensor time-synchronized to and spatially and mechanically separated from each other.
  • the first sensor is configured to measure data including a first signal related to a physiological signal of the living subject and a second signal
  • the second sensor is configured to measure data including at least the second signal.
  • the first signal measured by the first sensor has a signal strength greater than that the second signal measured by the first sensor.
  • the data measured by the first sensor and the second sensor of said sensor unit are processed such that subtraction of the second signal measured by the second sensor from the second signal measured by the first sensor results in a stronger first signal that is a signal of interest
  • the second signal is related to at least one of ambient, motion and vibration.
  • each of the first sensor and the second sensor comprises the IMU, a thermal sensor, a pressure sensor, and/or optical sensor.
  • each of the first sensor and the second sensor comprises the IMU.
  • the electronic device further comprises a plurality of thermal sensing units.
  • each thermal sensing units is embedded in a respective IMU.
  • the MCU operably receives inputs from synchronized outputs of a plurality of thermal sensor units with at least one thermal sensing unit for the ambient environment and at least one thermal sensing unit in direct thermal communication from the body isolated thermally from the ambient environment with in-sensor thermally isolating materials.
  • the electronic device is configured to automatically switch operation modes, the operation modes include at least a first mode when the living subject is at rest, and a second modes when the living subject is in a high motion.
  • the electronic device is configured to measure a range of physiological information from activity of a cardiopulmonary system and movements of a core body to a diverse collection of processes across thoracic cavity, esophagus, pharynx, and oral cavity related to respiration, speech, swallowing, wheezing, coughing, and sneezing.
  • the electronic device is configured to separate signals associated with the cardiopulmonary system and related processes from those due to body movements.
  • the electronic device is configured to spatiotemporally map movements of the skin at this region of the anatomy onto which the electronic device is attached during cardiac and respiratory activities.
  • the electronic device is configured to continuously measure temperature, heart rate (HR), respiratory rate (RR), activity level, and body orientation, across a range of vigorous activities and conditions. In one embodiment, the electronic device is configured to monitor key symptoms of a patient with COVID-19 infection to track progress of recovery and response to therapies in hospital and/or home. In one embodiment, the electronic device is configured to measure any of respiratory or motion related digital biomarkers associated with coughing, swallowing, and/or specific motion related activities. In one embodiment, the electronic device is configured to assess coughing when the living subject is moving or immobile, and/or to measure muscle motion, when the living subject is moving.
  • the electronic device further comprises a bidirectional wireless communication system electronically coupled to the electronic device and configured to send an output signal from the electronic device to an external device.
  • the external device is a mobile device, a computer, or a cloud service.
  • the bidirectional wireless communication system is further configured to deliver commands from the external device to the electronic device.
  • the bidirectional wireless communication system comprises a controller that utilizes at least one of NFC, Wi-Fi/Internet, Bluetooth, BLE, and cellular communication protocols for wireless communication.
  • the electronic device further comprises a customized app with a user interface deployed in the external device to allow a user to configure and operate the electronic device for data collection, data transfer, data storage and analysis, wireless charging, and monitoring of user’s conditions.
  • the customized app is configured to allow time-synchronized operation of a plurality of the sensor network simultaneously.
  • the electronic device further comprises a power module coupled to the sensor network for providing power thereto.
  • the power module comprises at least one battery for providing the power.
  • the battery is a rechargeable battery.
  • the power module further comprises a wireless charging module for wirelessly charging the rechargeable battery.
  • the power module further comprises a failure prevention element including a short-circuit protection component or a circuit to avoid battery malfunction.
  • the invention relates to an electronic device for measuring physiological parameters of a living subject, comprising a first sensor adapted for detecting a first group of data related to the living subject and a second group of data that is different from the first group of data; and a second sensor for detecting a third group of data that is substantially similar to the second group of data.
  • the first sensor and the second sensor are time- synchronized to allow the third group of data from the second sensor to be used to substantially cancel out the second group of data from the first sensor.
  • the first sensor and the second sensor are spatially and mechanically separated from each other. In one embodiment, the separation of the first sensor and the second sensor is greater than zero and less than a predetermined distance.
  • each of the first sensor and the second sensor comprises an IMU, a thermal sensor, a pressure sensor, or optical sensor.
  • the first group of data is physiological signals of the living subject
  • the second group of data is ambient signals at the first sensor.
  • the third group of data is ambient signals at the second sensor.
  • both of the first sensor and second sensor are operably in mechanical communication with the skin of the living subject.
  • the first sensor is operably in direct mechanical communication with the skin of the living subject for sensing physiological signals from the body
  • the second sensor is operably in indirectly mechanical communication with the skin of the living subject.
  • the first sensor and the second sensor are operably in directly mechanical communication with the skin of the living subject for sensing physiological signals from the body to assess pulse transit time.
  • the electronic device is flexible and conformable to the skin with a specific geometrical polarity for mounting in an anatomical location of interest of the living subject.
  • the invention relates to an electronic device for measuring physiological parameters of a living subject, comprising a first sensor adapted for detecting a first group of data related to the living subject and a second group of data that is different from the first group of data; and a second sensor for detecting a third group of data that is substantially similar to the second group of data, wherein in operation, the first sensor is positioned such that there is a first distance d1 between a center of the first sensor and an area of the living subject where physiological signals of the living subject are measurable; the second sensor is positioned such that there is a second distance d2 between a center of the second sensor and the center of the first sensor, wherein the second distance d2 is greater than zero and less than a predetermined distance.
  • the second sensor is positioned over the first sensor. In one embodiment, the second sensor is positioned away from the first sensor. In one embodiment, each of the first sensor and the second sensor comprises an IMU, a thermal sensor, or a pressure sensor. In one embodiment, the first group of data is physiological signals of the living subject, and the second group of data is signals related to ambient, motion and/or vibration at the first sensor. In one embodiment, the third group of data is signals related to ambient, motion and/or vibration at the second sensor. In one embodiment, both of the first sensor and second sensor are operably in mechanical communication with the skin of the living subject.
  • the first sensor is operably in directly mechanical communication with the skin of the living subject for sensing physiological signals from the body
  • the second sensor is operably in indirectly mechanical communication with the skin of the living subject.
  • both of the first sensor and the second sensor are operably in directly mechanical communication with the skin of the living subject for sensing physiological signals from the body to assess pulse transit time.
  • the electronic device is flexible and conformable to the skin with a specific geometrical polarity for mounting in an anatomical location of interest of the living subject.
  • FIGS. 1A-1F show images, schematic illustrations, functional flow charts, and mechanical modeling results for a wireless, skin-interfaced device designed for dual MA measurements at the SN and the SM, according to embodiments of the invention.
  • FIG.1A Image of the device mounted on the base of the neck, positioned with one end at the SN and the other at the SM.
  • FIG.1B Exploded-view schematic illustration of the active components, interconnect schemes, and enclosure architectures.
  • FIG.1C Image of a device next to a U.S. quarter (diameter, 24.26 mm).
  • FIG.1D Images of the device during various mechanical deformations: a twisting angle of 90°(left), 45% uniaxial stretching (middle), and a bending angle of 180° (right).
  • FIG.1E Finite element modeling of the mechanics for the deformations in FIG.1D. The contour plots show the maximum principle strains in the metal layer of the serpentine interconnects for twisting (left), stretching (middle), and bending (right).
  • FIG.1F Block diagram of the system operation.
  • a tablet provides an interface for operating the device, wirelessly downloading the data from the device, and transmitting these data to a cloud server through a cellular network.
  • FIGS. 2A-2G show a dual-sensing platform for differential temperature and MA sensing, according to embodiments of the invention.
  • FIG.2A Exploded-view and FIG.2B: cross-sectional schematic illustrations of the device.
  • FIG.2C Side view of a completed device next to a U.S. quarter.
  • FIG.2D Finite element results for the temperature distribution in the skin and outside the device for skin and ambient temperatures of 37° and 22°C, respectively, with a convection coefficient of 10 W m ⁇ 2 K ⁇ 1 .
  • FIG.2E Temperature profile along the A-B cross section for different ambient temperatures and convection coefficients.
  • FIG.2F Differential temperature measured using the temperature sensors in IMU1 and IMU2. (D) to (F) correspond to the case of a core body temperature of 37°C.
  • FIG.2G Representative results determined as the subject moves through rooms at various ambient temperatures. Dual temperatures (first row), differential temperature (second row), and the calibrated and measured core body temperatures (third row).
  • FIGS. 3A-3H show distributions of displacements across the neck and surrounding regions determined by 3D-PTV during natural respiratory and cardiac activities, with a focus on the SN and the SM, according to embodiments of the invention.
  • FIG.3A 3D vector and contour fields of displacements, superimposed on the neck image.
  • FIG.3B 3D view of FIG.3A.
  • the color denotes the velocity along the z axis, w, during a cardiac cycle.
  • FIG.3C Displacement along the z axis, ⁇ Z, as a function of time at the SN and SM during a breath hold, highlighting cardiac activity.
  • FIG.3D Differential displacement between the SN and SM determined from the data in FIG.3C.
  • FIG.3E Color contour of ⁇ Z at the peak of a cardiac cycle highlighted by the blue arrow in FIG.3D.
  • FIG.3F ⁇ Z as a function of time at the SN and SM during breathing and slight body motions.
  • FIG.3G Differential displacement between the SM and SN determined from the data in FIG.3F.
  • FIG.3H Color contour of ⁇ Z at the peak of inhalation, highlighted by the blue arrow in FIG.3G.
  • FIGS. 4A-4D show representative data collected during various ambulatory motions and measurements of controlled RR and normal HR, according to embodiments of the invention.
  • FIG.4A The subject sat quietly for 7 min, walked for 14 min with resting intervals, ran for 8 min with resting intervals, and jumped for 7 min with resting intervals under controlled RRs (6 to 35 RPM).
  • FIG.4B Magnified views of walking and running signals from (A), highlighting baseline fluctuations associated with respiration.
  • FIG. 4C Single-accelerometer data (black dot) yield reliable values of RR while the subject sits still. During ambulatory motions, the single-accelerometer data yield unreliable values of RR. The differential signals (blue dots) yield accurate respiration rates, consistent with ground truth (green triangles). The red arrow indicates the time frame of FIG.4B.
  • FIG.4D Single- accelerometer data provide the HR reliably while the subject sits still.
  • FIGS. 5A-5H show tracking of cardiopulmonary activity during intense physical activities, according to embodiments of the invention.
  • FIG.5A Image of the dual-sensing device at the SN/SM along with reference devices for SpO2 and electrocardiogram recording and thermocouples for oral and ambient temperature measurements while cycling.
  • FIG.5B Comparisons of RR and HR determined by the dual-sensing (blue square) and single-sensing (red circle) and reference devices (green triangle, for HR only) while cycling for 24 min.
  • FIG. 5C Image of the dual-sensing device on the SN/SM while playing basketball.
  • FIG.5D Comparisons of RR and HR determined from the dual- and single-sensing data while playing basketball for 11 min.
  • FIG.5E Image of the dual-sensing device on the SN/SM while swimming.
  • FIG.5F Comparisons of RR and HR determined with the dual- and single-sensing data while swimming for 5 min.
  • FIG.5G Representative z-axis acceleration data acquired from the dual-sensing device during swimming.
  • FIG.5H Magnified data associated with the differential signal (blue) and its baseline (light blue) from the area highlighted by the green box FIG.5G.
  • FIGS. 6A-6D show data collected from a COVID-19 patient in the form of cough count, RR, HR, activity level, and estimated core body temperature, according to embodiments of the invention.
  • FIG.6A Variation of cough frequency from the patient while recovering over a period of 8 days. The first set was measured from 1 to 7 p.m. on the first day. The second set was measured from 8 a.m. to 8 p.m. on the second day.
  • the third set was measured from 1 to 9 p.m. on the fourth day.
  • the fourth set was measured from 9 a.m. to 8 p.m. on the seventh day, and the fifth set was measured from 8 a.m. to 8 p.m. on the eighth day.
  • the purple line shows the cumulative number of coughs.
  • FIG.6B Variation of respiration rate and results from Savitzky- Golay smoothing (orange line).
  • FIG.6C Variation of HR and results from Savitzky-Golay smoothing (red line).
  • FIG.6D Activity level (green bar) and estimated core body temperature (red) during day (yellow shaded region) and night (blue shaded region). a.u., arbitrary units.
  • FIG.7 shows devices under CDC guided cleaning and disinfecting process with 70% alcohol solution.
  • FIG.8 shows a layout of the flexible PCB for the dual-sensing device, according to embodiments of the invention. Red dashed lines show the folding planes and yellow dashed line shows the actual device size after folding and encapsulation.
  • FIG.9 shows time synchronized operation of 12 devices for whole body acceleration measurements, according to embodiments of the invention.
  • FIG.10 shows a dual-sensing system state diagram, according to embodiments of the invention.
  • FIG.13 shows temperature color mapping of IMU1 and IMU2 along A-B cross-section under different ambient temperatures (Tamb: from 18 °C to 24 °C) and convection coefficients (5W/m 2 K to 30 W/m 2 K).
  • FIG.14 shows a 1-D heat transfer model, according to embodiments of the invention.
  • A Illustration of the heat transfer model.
  • B Thickness and thermal conductivity of each material layer.
  • FIG.15 shows representative dual-sensing data collected from a subject during movement through rooms at various ambient temperatures.
  • FIG.17 shows temperature results from the 1-D analytical model and 3-D FEA model. (A) IMU1 and IMU2 temperatures from the 1-D analytical model and the 3-D FEA model.
  • FIG.18 shows 3D-PTV measurements.
  • A Photograph and (B) illustration of the experimental setup.
  • C Velocity along the z-axis, w vs time at the SN (IMU1 location) and SM (IMU2 location).
  • FIG.19 shows dual-sensing data collected during 3D-PTV measurements.
  • A Acceleration along the z-axis vs time at the SN and SM during breathing.
  • B Calculated velocity along the z- axis of (A).
  • C Calculated displacement along the z-axis of (A).
  • D Differential displacement: SM – SN of (C).
  • FIG.20 shows simplified 1-D Analytical model of differential accelerometry.
  • A Schematic illustration of the simplified 1-D Analytical Model for cardiac activity. A displacement is applied to the rigid platform causing sensors IMU1 and IMU2 (with mass m) to accelerate in the z-axis. IMU1 is tied to the platform and IMU2 is connected to the platform by a spring of stiffness k and damper with damping ratio ⁇ .
  • B Analytical result of z-axis displacement from IMU1 and IMU2 during a cardiac cycle without respiratory activity.
  • C Differential displacement between IMU1 and IMU2 determined from the data in (B).
  • D Differential displacement between IMU1 and IMU2 determined from the data during respiratory activities.
  • FIG.21 shows data collected from different body orientations with packaged and unpackaged devices.
  • A -(D) Measured signal from an unpackaged dual-sensing device.
  • A The subject sat quietly for 45 seconds, leaned back for 70 seconds, leaned forward for 35 seconds under normal and held breath conditions.
  • B Magnified views of heart and respiratory activities from (A).
  • C Flipped dual-sensing orientation. IMU1 was placed one the SM and IMU2 was placed on the SN. The subject sat quietly for 20 seconds, leaned back for 25 seconds, leaned forward for 30 seconds under normal and held breath conditions.
  • D Magnified views of heart and respiratory activities from (C).
  • E -(H) Measured signal from a packaged dual-sensing device.
  • FIG.22 shows algorithm for determining the respiratory rate from the differential signal.
  • A Block diagram of signal processing flow in the frequency domain.
  • B Differential data derived from IMU1 and IMU2.
  • FIG.26 shows measurement setup for the stationary bike riding.
  • FIG.27 shows measured data from the stationary bike riding.
  • A Z-axis acceleration while riding a stationary bike for 24 minutes.
  • B Dual and reference temperature values while riding a stationary bike.
  • FIG.28 shows measured data during push-ups with a controlled respiratory rate.
  • A Z- axis acceleration while performing push-ups under controlled respiration cycles.
  • B Baseline of z-axis acceleration.
  • FIG.29 shows measured data during hammering nails, carrying boxes, and shoveling dirt.
  • A Z-axis acceleration while hammering nails.
  • B Z-axis acceleration while lifting, carrying, and placing a box.
  • FIG.30 shows motion artifacts from local movement. Local motion induced from movements of the neck.3-axis acceleration data from the IMU1 on SN.
  • FIG.31 shows feature classification with support vector machine (SVM).
  • A Flowchart for event extraction using adapted thresholds from raw data.
  • B Acceleration data recorded over 50 s with various activities that include tapping, coughing, laughing, and throat clearing. The blue line is the time series results of acceleration along the z-axis. The orange solid line is the envelope of the signal. The yellow line is the adapted threshold to detect specific features. The red dot is thcenter of the detected event.
  • (C) Extracted samples after peak detection (1 st row), FFT (2 nd row), and spectrograms (3 rd row) of coughing, throat clearing, laughing and tapping.
  • (D) Binary tree architecture design with SVM for classifying these activities.
  • FIG.32 shows data set for developing the classifier.
  • FIG.33 shows device temperature monitoring.
  • A Experimental setup for monitoring the temperature of the device, with a focus on the battery.
  • B Battery temperature measurement screen after 8 minutes of operation at room temperature.
  • C Change in battery temperature over this time period.
  • FIGS.34A-34C show schematically an electronic device according to various embodiments of the invention. DETAILED DESCRIPTION OF THE INVENTION The invention will now be described more fully hereinafter with reference to the accompanying drawings, in which exemplary embodiments of the invention are shown.
  • accelerometers are preferably high frequency, three-axis accelerometers, capable of detecting a wide range of mechano-acoustic signals. Examples include respiration, swallowing, organ (lung, heart) movement, motion (scratching, exercise, and/or movement), talking, bowel activity, coughing, sneezing, and the like.
  • the term “bidirectional wireless communication system” refers to onboard components of sensors, wireless controller and other electronic components that provides capability of receiving and sending signals using at least one communication protocol of near field communication (NFC), Wi-Fi/Internet, Bluetooth, Bluetooth low energy (BLE), and Cellular communication protocols for wireless communication.
  • NFC near field communication
  • Wi-Fi/Internet Wireless Fidelity
  • Bluetooth Bluetooth low energy
  • BLE Bluetooth low energy
  • an output may be provided to an external device, including a cloud-based device, personal portable device, or a caregiver’s computer system.
  • a command may be sent to the sensor, such as by an external controller, which may or may not correspond to the external device.
  • Machine learning algorithms may be employed to improve signal analysis and, in turn, command signals sent to the medical sensor, including a stimulator of the medical sensor for providing haptic signal to a user of the medical device useful in a therapy. More generally, these systems may be incorporated into a processor, such as a microprocessor located on-board or physically remote from the electronic device of the medical sensor.
  • a processor such as a microprocessor located on-board or physically remote from the electronic device of the medical sensor.
  • An example of the wireless controller is a near field communication (NFC) chip, including NFC chips.
  • NFC near field communication
  • NFC is a radio technology enabling bi- directional short range wireless communication between devices.
  • a wireless controller is a Bluetooth® chip, or a BLE system-on-chip (SoC), which enables devices to communicate via a standard radio frequency instead of through cables, wires or direct user action.
  • SoC BLE system-on-chip
  • a flexible material, structure, device or device component may be deformed into a curved shape without introducing strain larger than or equal to 5%, for some applications larger than or equal to 1%, and for yet other applications larger than or equal to 0.5% in strain-sensitive regions.
  • a used herein, some, but not necessarily all, flexible structures are also stretchable.
  • a variety of properties provide flexible structures (e.g., device components) of the invention, including materials properties such as a low modulus, bending stiffness and flexural rigidity; physical dimensions such as small average thickness (e.g., less than 100 microns, optionally less than 10 microns and optionally less than 1 micron) and device geometries such as thin film and open or mesh geometries.
  • stretchable refers to the ability of a material, structure, device or device component to be strained without undergoing fracture.
  • a stretchable material, structure, device or device component may undergo strain larger than 0.5% without fracturing, for some applications strain larger than 1% without fracturing and for yet other applications strain larger than 3% without fracturing.
  • many stretchable structures are also flexible.
  • Some stretchable structures e.g., device components
  • Stretchable structures include thin film structures comprising stretchable materials, such as elastomers; bent structures capable of elongation, compression and/or twisting motion; and structures having an island – bridge geometry.
  • Stretchable device components include structures having stretchable interconnects, such as stretchable electrical interconnects. As used herein, for embodiments where the devices are mounted directly to the skin, the devices may be characterized as stretchable, including stretchable and flexible so as to achieve good conformal contact with underlying skin, if desired.
  • Conformable refers to a device, material or substrate which has a bending stiffness sufficiently low and elasticity sufficiently high to allow the device, material or substrate to adopt a desired contour profile, including a contour profile that may change over time, for example a contour profile allowing for conformal contact with a surface having a pattern of relief or recessed features, or.
  • a desired contour profile is that of a tissue in a biological environment, for example skin or the epidermal layer.
  • Useful elastomers include those comprising polymers, copolymers, composite materials or mixtures of polymers and copolymers.
  • Elastomeric layer refers to a layer comprising at least one elastomer. Elastomeric layers may also include dopants and other non-elastomeric materials.
  • elastomers useful include, but are not limited to, thermoplastic elastomers, styrenic materials, olefenic materials, polyolefin, polyurethane thermoplastic elastomers, polyamides, synthetic rubbers, PDMS, polybutadiene, polyisobutylene, poly(styrene-butadiene-styrene), polyurethanes, polychloroprene and silicones.
  • an elastomeric stamp comprises an elastomer.
  • Exemplary elastomers include, but are not limited to silicon containing polymers such as polysiloxanes including poly(dimethyl siloxane) (i.e.
  • a flexible polymer is a flexible elastomer.
  • encapsulate refers to the orientation of one structure such that it is at least partially, and in some cases completely, surrounded by one or more other structures. “Partially encapsulated” refers to the orientation of one structure such that it is partially surrounded by one or more other structures. “Completely encapsulated” refers to the orientation of one structure such that it is completely surrounded by one or more other structures.
  • the invention includes devices having partially or completely encapsulated electronic devices, device components and/or inorganic semiconductor components. Embodiments of the invention are illustrated in detail hereinafter with reference to accompanying drawings. The description below is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses.
  • One of the objectives of this invention is to provide a new class of wearable sensors that offers dramatically improved motion-resistant and ambient temperature resistant sensing enabled by novel device mechanics and design, and algorithms to subtract noises.
  • This new class of wearable leverages differential measurement of outputs from sensors where one sensor is measuring physiological signals from the body and related ambient and gross body motion signals, and another sensor is measuring at least the related ambient and gross body motion signals allow for effective elimination of noise, e.g., related ambient and gross body motion signals, during rest and motion.
  • the invention relates to an electronic device for measuring physiological parameters of a living subject.
  • the electronic device in one embodiment includes at least a first inertial measurement unit (IMU) and a second IMU, the first IMU and the second IMU are time-synchronized to and spatially and mechanically separated from each other; and a microcontroller unit (MCU) electronically coupled to the first IMU and the second IMU for processing of data streams from the first IMU and the second IMU.
  • the first IMU is configured to measure data including a first signal related to a physiological signal of the living subject and a second signal
  • the second IMU is configured to measure data including at least the second signal.
  • the first signal measured by the first IMU has a signal strength greater than that the second signal measured by the first IMU.
  • the data measured by the first IMU and the second IMU are processed such that subtraction of the second signal measured by the second sensor from the second signal measured by the first sensor results in a stronger first signal that is a signal of interest.
  • the second signal is related to at least one of ambient, motion and vibration.
  • the data measured by the second IMU includes the first signal and the second signal.
  • a signal-to-noise ratio (SNR) of a signal measured by the first IMU and the second IMU together is lower than a first SNR of a signal measured by the first IMU individually, or a second SNR of a signal measured by the second IMU individually.
  • SNR signal-to-noise ratio
  • both of the first IMU and the second IMU are operably in mechanical communication with the skin of the living subject.
  • one of the first IMU and the second IMU is operably in directly mechanical communication with the skin of the living subject for sensing physiological signals of the body, while the other of the first IMU and the second IMU is operably in indirectly mechanical communication with the skin of the living subject.
  • the first IMU and the second IMU are operably in directly mechanical communication with the skin of the living subject.
  • one of the first IMU and the second IMU is separated from the rest of rigid components of the electronic device.
  • the electronic device also comprises at least first and second thermal sensing units, wherein one of the first and second thermal sensing units is thermally isolated from an ambient environment and configured to measure a body temperature of the living subject, and the other of the first and second thermal sensing units is configured to measure the ambient temperature.
  • each of the first and second thermal sensing units is embedded in a respective one of the first and second IMUs.
  • the electronic device is configured to measure a range of physiological information from activity of a cardiopulmonary system and movements of a core body to a diverse collection of processes across thoracic cavity, esophagus, pharynx, and oral cavity related to respiration, speech, swallowing, wheezing, coughing, and sneezing.
  • the electronic device is configured to separate signals associated with the cardiopulmonary system and related processes from those due to body movements.
  • the electronic device is configured to spatiotemporally map movements of the skin at this region of the anatomy onto which the electronic device is attached during cardiac and respiratory activities.
  • the electronic device is configured to continuously measure temperature, heart rate (HR), respiratory rate (RR), activity level, and body orientation, across a range of vigorous activities and conditions.
  • the electronic device is configured to monitor key symptoms of a patient with COVID-19 infection to track progress of recovery and response to therapies in hospital and/or home.
  • the electronic device is configured to measure any of respiratory or motion related digital biomarkers associated with coughing, swallowing, and/or specific motion related activities.
  • the electronic device is configured to assess coughing when the living subject is moving or immobile, and/or to measure muscle motion, when the living subject is moving.
  • the electronic device further comprises a bidirectional wireless communication system electronically coupled to the electronic device and configured to send an output signal from the electronic device to an external device.
  • the external device is a mobile device, a computer, or a cloud service.
  • the bidirectional wireless communication system is further configured to deliver commands from the external device to the electronic device.
  • the bidirectional wireless communication system comprises a controller that utilizes at least one of near field communication (NFC), Wi-Fi/Internet, Bluetooth, Bluetooth low energy (BLE), and cellular communication protocols for wireless communication.
  • NFC near field communication
  • Wi-Fi/Internet Wireless Fidelity
  • Bluetooth Bluetooth low energy
  • the electronic device further comprises a customized app with a user interface deployed in the external device to allow a user to configure and operate the electronic device for data collection, data transfer, data storage and analysis, wireless charging, and monitoring of user’s conditions.
  • the customized app is configured to allow time-synchronized operation of a plurality of the electronic devices simultaneously.
  • the electronic device further comprises a power module coupled to the first IMU, the second IMU and the MCU for providing power thereto.
  • the power module comprises at least one battery for providing the power.
  • the battery is a rechargeable battery.
  • the power module further comprises a wireless charging module for wirelessly charging the rechargeable battery.
  • the power module further comprises a failure prevention element including a short-circuit protection component or a circuit to avoid battery malfunction.
  • the second IMU is placed in a manner that it bends and folds over the battery.
  • the electronic device further comprises a flexible printed circuit board (fPCB) having flexible and stretchable interconnects electrically connecting to electronic components including the first IMU, the second IMU and the MCU and the power module.
  • the electronic device further comprises an elastomeric encapsulation layer at least partially surrounding the electronic components and the flexible and stretchable interconnects to form a tissue-facing surface attached to the living subject and an environment-facing surface, wherein the tissue-facing surface is configured to conform to a skin surface of the living subject.
  • the encapsulation layer is formed of a flame retardant material.
  • the elastomeric encapsulation layer is a waterproof and biocompatible silicone enclosure.
  • the electronic device further comprises a biocompatible hydrogel adhesive for attaching the electronic device on the respective region of the living subject, wherein the biocompatible hydrogel adhesive is adapted such that signals from the living subject are operably conductible to the first IMU and the second IMU.
  • the electronic device is flexible and conformable to the skin with a specific geometrical polarity for mounting in an anatomical location of interest of the living subject.
  • the electronic device is wearable, tissue mountable or in mechanical communication or direct mechanical communication with the skin of the living subject.
  • mechanical communication refers to the ability for the sensors to interface directly or indirectly with the skin or other tissue in a conformable, flexible, and direct manner (e.g., there is no air gap) which in some embodiments allows for deeper insights and better sensing with less motion artifact compared to accelerometers strapped to the body (wrists or chest).
  • the electronic device is twistable stretchable, and/or bendable.
  • Various embodiments of the present technology include a soft, conformal, stretchable class of device configured specifically for mechano-acoustic recording from the skin, capable of being used on nearly any part of the body, in forms that maximize detectable signals and allow for multimodal operation, such as electrophysiological recording, and neurocognitive interaction.
  • Another aspect of the invention provides an electronic device comprising a sensor network comprising a plurality of sensor units operably deployed on a skin of the living subject, the plurality of sensor units being time-synchronized to and spatially and mechanically separated from each other; and an MCU electronically coupled to the plurality of sensor units for processing of data streams from the plurality of sensor units.
  • the plurality of sensor units are configured to measure a same physiological parameter, or different physiological parameters.
  • each of the plurality of sensor units comprises at least a first sensor and the second sensor time-synchronized to and spatially and mechanically separated from each other.
  • the first sensor is configured to measure data including a first signal related to a physiological signal of the living subject and a second signal
  • the second sensor is configured to measure data including at least the second signal.
  • the first signal measured by the first sensor has a signal strength greater than that the second signal measured by the first sensor.
  • the data measured by the first sensor and the second sensor of said sensor unit are processed such that subtraction of the second signal measured by the second sensor from the second signal measured by the first sensor results in a stronger first signal that is a signal of interest
  • the second signal is related to at least one of ambient, motion and vibration.
  • each of the first sensor and the second sensor comprises the IMU.
  • the electronic device further comprises a plurality of thermal sensing units.
  • the MCU operably receives inputs from synchronized outputs of a plurality of thermal sensor units with at least one thermal sensing unit for the ambient environment and at least one thermal sensing unit in direct thermal communication from the body isolated thermally from the ambient environment with in-sensor thermally isolating materials.
  • the electronic device is configured to automatically switch operation modes, the operation modes include at least a first mode when the living subject is at rest, and a second modes when the living subject is in a high motion.
  • Yet another aspect of the invention provides an electronic device 1001 for measuring physiological parameters of a living subject 1000, as shown in FIGS.34A-34C.
  • the electronic device 1001 comprises a first sensor 1002 adapted for detecting a first group of data related to the living subject 1000 and a second group of data that is different from the first group of data; and a second sensor 1004 for detecting a third group of data that is substantially similar to the second group of data.
  • the first sensor 1002 and the second sensor 1004 are time- synchronized to allow the third group of data from the second sensor to be used to substantially cancel out the second group of data from the first sensor.
  • the first sensor and the second sensor are spatially and mechanically separated from each other. In certain embodiments, the separation of the first sensor and the second sensor is greater than zero and less than a predetermined distance.
  • each of the first sensor and the second sensor comprises an IMU, a thermal sensor, and/or a pressure sensor.
  • the first group of data is physiological signals of the living subject
  • the second group of data is signals related to ambient, motion and/or vibration at the first sensor.
  • the third group of data is signals related to ambient, motion and/or vibration at the second sensor.
  • both of the first sensor and second sensor are operably in mechanical communication with the skin of the living subject.
  • the first sensor is operably in directly mechanical communication with the skin of the living subject for sensing physiological signals from the body, and the second sensor is operably in indirectly mechanical communication with the skin of the living subject.
  • the first sensor and the second sensor are operably in directly mechanical communication with the skin of the living subject for sensing physiological signals from the body to assess pulse transit time.
  • the electronic device is flexible and conformable to the skin with a specific geometrical polarity for mounting in an anatomical location of interest of the living subject.
  • a further aspect of the invention provides an electronic device 1001 for measuring physiological parameters of a living subject 1000, as shown in FIGS.34A-34C.
  • the electronic device 1001 comprises a first sensor 1002 adapted for detecting a first group of data related to the living subject 1000 and a second group of data that is different from the first group of data; and a second sensor 1004 for detecting a third group of data that is substantially similar to the second group of data, wherein in operation, the first sensor 1002 is positioned such that there is a first distance d1 between a center of the first sensor 1002 and an area of the living subject 1000 where physiological signals of the living subject 1000 are measurable; the second sensor 1004 is positioned such that there is a second distance d2 between a center of the second sensor 1004 and the center of the first sensor 1002, wherein the second distance d2 is greater than zero and less than a predetermined distance.
  • the second sensor 1004 is positioned over the first sensor 1002, as shown in FIG.34A. In one embodiment, the second sensor 1004 is positioned away from the first sensor 1002, as shown in FIGS.34B-34C.
  • measurements of physiological parameters can be derived from mechano-acoustic signals from the human body of heart rate, respiratory rate, body position, swallow count, cry time, talk time, singing, coughing, and differential motion of specific body parts when the sensor is mounted across an anatomical boundary (e.g., trunk motion in relation to the head, hand motion in relation to the wrist, lower leg motion in relation to the knee), and other respiratory signals at rest and during motion.
  • anatomical boundary e.g., trunk motion in relation to the head, hand motion in relation to the wrist, lower leg motion in relation to the knee
  • the derivation of these physiological resistant to motion allows for applicability across a wide range of medical specialties and acuity ranging from critical care, general medicine care, ambulatory medicine, rehabilitation, and consumer health particularly in high motion scenarios.
  • the core body sensing is resistant to ambient temperature fluctuations and clothing.
  • the single MCU receives inputs from synchronized outputs of a plurality of IMU sensors where an individual IMU sensor is in differential mechanical communication with the body.
  • the single MCU receives inputs from synchronized outputs of a plurality of thermal sensors with at least one thermal sensor for the ambient environment and at least one thermal sensor in direct thermal communication from the body isolated thermally from the ambient with in-sensor thermally isolating materials.
  • the novel mechanics of the sensor/device allows for twisting, stretching, bending to enable a low profile design and thermal / mechanical isolation of various sensing elements in the device.
  • the novel mechanics of the sensor/device enables a physical separation of rigid components of the device and the sensing element enabling mounting in unique anatomical locations for high data fidelity. Further advantages include the ability to obscure the sensor from sight to reduce patient stigma. This represents an umbilical functionality to allow for discrete sensing in sensitive locations with the body of the sensor is mounted in a location easier to obscure with clothing.
  • thermal isolating materials and layers that allow for improved thermal sensing of core body temperature that is resistant to ambient temperature fluctuations.
  • the senor/device is configured such that the modes of operation in situations of high motion allows for automated switching to motion resistant outputs.
  • the preferred measurement of heart rate may be ECG at rest – however, a patient maybe in a situation where they are actively moving.
  • the sensor can start actively interrogating the dual IMUs for heart rate derivation where ECG based heart rate is not dependable.
  • the sensor/device has ability to toggle or activate dual sensing functionality in situations of high motion to improve accuracy but conserve power in settings of rest.
  • the techniques introduced here can be embodied as special purpose hardware (e.g. circuitry) as programmable circuitry appropriately programmed with software and/or firmware, or as a combination of special-purpose and programmable circuitry.
  • embodiment may include a machine-readable medium having stored thereon instructions which may be used to program a computer (or other electronic devices) to perform a process.
  • the machine-readable medium may include, but is not limited to, floppy diskettes, optical disks, compact disc read-only memories (CD-ROMs), magneto-optical disks, ROMs, random access memories (RAMs), erasable programmable read-only memories (EPROMs), electrically erasable programmable read-only memories (EEPROMs), magnetic or optical cards, flash memory, or other type of media / machine-readable medium suitable for storing electronic instructions.
  • these dual-sensing devices When mounted at a location that spans the suprasternal notch and the sternal manubrium, these dual-sensing devices allow measurements of heart rate and sounds, respiratory activities, body temperature, body orientation, and activity level, along with swallowing, coughing, talking, and related processes, without sensitivity to ambient conditions during routine daily activities, vigorous exercises, intense manual labor, and even swimming. Deployments on patients with COVID-19 allow clinical-grade ambulatory monitoring of the key symptoms of the disease even during rehabilitation protocols.
  • the exemplary work exploits a pair of time-synchronized, high-bandwidth accelerometers (inertial measurement units (IMUs)) at opposite ends of a skin-interfaced device that locates one of the IMUs at the suprasternal notch (SN) and the other at the sternal manubrium (SM). Differences in movements of the skin associated with cardiac and respiratory activity between these regions lead to differences in signals captured by these IMUs. By contrast, overall movements of the neck and the core of the body produce nearly identical responses. As a consequence, simple differential measurements cleanly eliminate common mode features, thereby separating signals associated with cardiopulmonary and related processes from those due to body movements.
  • IMUs intial measurement units
  • thermo sensors integrated in these IMUs can be used in a similar differential manner to yield estimates of core body temperature, largely independent of the ambient.
  • careful choices in thermal aspects of the device layout, rather than intrinsic anatomical gradients, produce the necessary differential responses.
  • the following sections present (i) designs of automated devices that incorporate matched pairs of high-bandwidth IMUs with optimized soft mechanics for high measurement sensitivity and accurate time synchronization across the SN and SM; (ii) results of spatiotemporal mapping of movements of the skin at this region of the anatomy during cardiac and respiratory activity; (iii) examples of modeling and design approaches for exploiting these IMUs in dual temperature sensing of core body temperature, with minimal influence of the thermal ambient; (iv) demonstrations of continuous, differential measurements of temperature, HR, and respiratory rate (RR) across a range of vigorous activities and conditions, with benchmarking against the most accurate commercial sensors; and (v) illustrations of the use on patients recovering from COVID-19 infections to track key symptoms of the disease even during intense physical rehabilitation protocols.
  • FIG.1A shows a device mounted on the base of the neck, positioned to span the SN and SM.
  • FIG.1B presents an exploded-view schematic illustration of the soft enclosure and the fPCB with passive/active chip-scale components.
  • Top and bottom encapsulating films of a silicone elastomer mechanically isolate the active parts of the systems in a sealed enclosure that allows operation even when submerged in water or exposed to sweat.
  • the design is also compatible with U.S. Centers for Disease Control and Prevention guidelines for cleaning and disinfecting using 70% alcohol solutions (FIG.7).
  • the fPCB exploits a copper (12 ⁇ m)–polyimide (PI; 25 ⁇ m)–copper (12 ⁇ m) laminate (DuPont, AP7164R) patterned to define conductive traces with widths of 80 and 150 ⁇ m.
  • the layout (FIG. 8) includes separate islands for the circuit components (main body), each of the two IMUs (IMU1 and IMU2), and a wireless charging coil. Serpentine-shaped traces interconnect these islands to mechanically decouple the IMUs from one another, as necessary in precision, differential measurements of MA signals at the surface of the skin.
  • the fPCB also includes multiple zones to allow for static bending during an assembly process that folds the system into a compact configuration.
  • the image in FIG.1C shows the overall size relative to a U.S. quarter (diameter, 24.26 mm).
  • the dimensions of the encapsulated device are 46 mm by 22 mm; its thickness is less than 9 mm, and its weight is less than 6.35 g.
  • the block diagram in FIG.1F summarizes the overall system operation.
  • the three main components include the device, a tablet with a customized app as a user interface, and a cloud platform for data storage and analytics.
  • the device uses a BLE SoC (Bluetooth Low Energy System on a Chip) (Nordic Semiconductor, nRF52840), a PMIC (power management integrated circuit) (Texas Instruments, BQ25120), a 4-gigabit NAND flash memory (Micron, MT29F4G01), and two identical IMUs, each with an embedded temperature sensing unit (STMicroelectronics, LSM6DSL).
  • Wireless charging involves voltage and current protection as support for a 75-mA ⁇ hour lithium polymer battery.
  • the user interface allows time-synchronized operation of up to 12 devices, simultaneously. Although not explored in the following experiments in this study, this feature supports monitoring of social interactions and/or capture of MA signals at multiple body locations (FIG.9).
  • FIG.10 describes the state diagram of the system, to illustrate behaviors before and after configuration, followed by deployment on a subject.
  • the diagram also shows operation during data collection, charging, and data transfer. Full automation of the key operational steps minimizes user burden, of particular importance for use with patients with COVID-19, as described subsequently.
  • the user simply mounts the device during use and places it on the wireless charging platform when removed.
  • the sensor continuously stores data from both accelerometers onto the internal memory module when not on the charging platform; when on this platform, the device charges and simultaneously streams data to a user interface device via Bluetooth protocols.
  • the user interface then passes data to a cloud hub for signal processing to extract various physiological information, including cough count, RR, HR, activity level, body orientation, and calibrated body temperature.
  • the cloud hub is HIPAA (Health Insurance Portability and Accountability Act) compliant, and the interface application uses HTTPS transport layer security (TLS 1.2) with an algorithm for encryption/decryption for the application programming interface and a standard for in-storage encryption (AES-256).
  • TLS 1.2 HTTPS transport layer security
  • AES-256 a standard for in-storage encryption
  • Core Body Temperature Estimation with Dual Temperature Sensing The simplest consequence of the dual-sensing architecture is in temperature measurements that approximate the temperature of the skin (T skin ), largely unperturbed by the ambient (Tamb), following schemes described previously in other contexts.
  • sensors embedded in IMU1 and IMU2 in a configuration illustrated in FIG.2A, yield temperatures with repeatability of 0.004°C every 5 s (adjustable up to a 52-Hz sampling rate).
  • IMU1 rests directly adjacent to the skin, separated only by the thin bottom encapsulation layer (0.3-mm-thick silicone elastomer).
  • a 6-mm-thick thermally insulating foam (polyurethane mixture) with a metallic film (12- ⁇ m-thick aluminized polyethylene) minimizes coupling to the environment via convection, conduction, and radiation.
  • the temperature at IMU1 depends strongly on the core body temperature, modulated by the effective thermal properties of the tissues and the ambient conditions.
  • IMU2 resides on the outward-facing side of the device, with only the top encapsulation layer above, to maximize and coupling to the environment (FIGS.2B-2C).
  • the multiple underlying layers including the adhesive film, bottom encapsulation, fPCB, battery, and thermal insulating foam limit heat transfer from the skin to IMU2.
  • Transient heat transfer analysis associated with three-dimensional (3D) thermal conduction and natural convection quantifies these effects.
  • the boundary conditions include a constant temperature at the bottom surface of the tissue layer (T core ) and convective coupling to the ambient air at the free surfaces (T amb ).
  • the analysis also quantifies the effects of changes in the ambient temperature, the core body temperature, the convection coefficient, and other key parameters. Measurements of differential temperature together with subject-specific thermal models yield robust estimates of core body temperature.
  • a simple demonstration involves a subject wearing a device in an environment with an ambient temperature of 18.2°C, then moving between areas with temperatures of 21.3° and 19.5°C every 3 to 8 min, and lastly remaining in place as the ambient temperature rises from 19.5° to 24.2°C for 7 min.
  • the results for temperatures recorded from IMU1 and IMU2 appear in the top graph in FIG.2G.
  • the middle graph shows the differential temperature.
  • a subject-specific model converts these temperature measurements into estimates of core body temperature (third row in FIG.2G), determined by eq.
  • T core T amb +(T IMU1 ⁇ T IMU2 ) ⁇ (B/A ⁇ D/C), where T amb is the ambient temperature inferred from IMU2, T IMU1 is the temperature from IMU1, T IMU2 is the temperature from IMU2, and (B/A ⁇ D/C) is a quantity that depends on the thickness and heat transfer coefficients of the skin and the various material layers of the device. Details of the structures, values, equations, and the modeling approaches appear in FIG.14 (see the 1-D analytical model for the thermal characteristics section in Materials and Methods).
  • thermocouple placed under the tongue yields reference values that approximate the core body temperature.
  • FIG.15 compares the results to the core temperature estimated from measurements at IMU1 and IMU2. The differences remain less than ⁇ 0.5°C across ambient temperatures from 19.5° to 24.2°C.
  • FIG.16 shows Bland-Altman plots of the data. Sensing with only IMU1 (red) yields a mean difference of 3.81°C and an SD of 0.22°C compared to the oral measurement; the dual-sensing approach (blue) yields a mean difference of 0.01°C and an SD of 0.18°C.
  • a 1D heat transfer model for analytics and 3D FEA model of the temperature dynamics (FIG.17) can capture essential aspects of these demonstrations.
  • the analytical and 3D FEA results agree well over the range of different ambient temperature scenarios with relevant heat convection coefficients and the core temperature (36.3°C), similar to the experimental results in FIGS.2G and 17 (B).
  • Dual Sensing from the SN and the SM Dual temperature sensing relies critically on design choices that yield different levels of sensitivity to temperatures of the body and the ambient for IMU1 and IMU2.
  • differential responses arise mainly from spatial gradients in motions across the mounting location, specifically those from the SN, the location of IMU1, and from the SM, the location of IMU2 (2.5 cm below the IMU1).
  • 3D particle tracking velocimetry 3D particle tracking velocimetry
  • 3D-PTV relies on optical techniques to track the Lagrangian paths of fiducial marks on the skin, in 3D using stereoscopic imaging, in a way that recapitulates the point-measurement modality of the IMUs.
  • 3D-PTV can capture the essence of dual sensing from the SN and SM by recording from four time-synchronized, high-speed cameras, each at a frame rate of 200 frames per second (fps) (FIG.18), and track motions across the neck, including regions of the SN and SM (FIG.3A).
  • FIGS.3C-3E show additional detail, corresponding to z-axis displacement profiles through several cardiac cycles during a breath hold after a brief period of exercise (20 push-ups).
  • the peak displacements at the SN are ⁇ 50% larger than those at the SM, as shown in FIG.3C.
  • FIG.3D shows a color contour plot of z-axis displacements at the peak of the cardiac cycle highlighted by the arrow in FIGS.3D and 18 (C-D). Similar considerations apply to differential dynamics associated with respiration.
  • FIGS. 3F-3H and 18 (E-F) summarize the displacement distributions for three cycles of breathing while slightly swinging back and forth along the z axis.
  • FIG.3F shows motions at the SN and SM, where responses include contributions from body motions and respiration for each case.
  • the differential result shown in FIG.3G largely isolates the respiratory signals, as shown in FIG.18 (E-F). Note that the small periodic features in these data arise from cardiac activity. A color contour plot of z-axis displacements at peak inhalation further highlights the spatial gradients that enable differential detection, as shown in FIG.3H. Data captured with the devices show similar trends (FIG.19), and simple 1D analytical models (FIG.20; see the “Analytical modeling of differential accelerometry” section in Materials and Methods) can capture essential aspects of these behaviors (FIG.21) that reveal a clear basis for differential detection at the SN and SM.
  • FIGS.4A-4D summarizes results captured using a device platform that incorporates IMUs with capabilities in high-fidelity three-axial accelerometry.
  • the flow chart in FIG.22 (A) illustrates the approach for calculating the RR (respirations per minute (RPM)) from the differential data.
  • the algorithm selects and performs a weighted average of the five highest energy components (minimum threshold of 50% of the maximum energy) in the frequency spectrum across the range of interest for the RR (6 to 60 RPM) within 1-min time windows, as shown in FIG.22 (F).
  • the differential signal largely eliminates common-mode “noise” associated with walking.
  • Other signal components such as those due to cardiac activity lie outside the frequency range associated with respiration and/or have power below the threshold, as shown in FIG.22 (D-E).
  • the flow chart in FIG.23 (A) highlights the corresponding algorithm for HR (beats per minute (BPM)).
  • HR beats per minute
  • FIG.23 (B-C) Band-pass filtering of the frequency spectra for 1-min time windows with cutoff frequencies of 45 and 170 BPM eliminates low-frequency signals from slow body processes and high-frequency content from vocalization and related events, as shown in FIG.23 (B-C).
  • This frequency envelope captures essential features associated with the S1 peaks associated with cardiac sounds, equivalent to those observed in seismocardiograms (SCGs) , as shown in FIG.23 (D).
  • SCGs seismocardiograms
  • FIG.23 (E) The frequency with the maximum energy and those with at least 80% of the maximum energy serve (FIG.23 (E)) as the basis for a weighted average to determine the HR.
  • FIGS.4B-4D highlight results obtained during sitting, walking, running, and jumping.
  • the first instance involves resting in a chair with a controlled RR of 6, 10, 12, 15, 20, 30, and 35 RPM (0 to 7 min).
  • the subject intentionally controls the exhale/inhale (1:1 ratio) time with a timer while moving.
  • the subject walks (8 to 21 min, 90 steps/min with 50-cm average stride lengths), runs (22 to 29 min, 180 steps/min with 85-cm average stride lengths), and jumps (31 to 36 min, vertical jumps every 2 to 3 s at approximately 40-cm height), all under similar controlled RR. Walking and running generate repetitive sequences of high-amplitude, impulse signals that dominate the data from IMU1 (red) and IMU2 (black).
  • the differential signals feature a clear, periodic response associated with respiration (15 BPM; blue), as shown in the left frame in FIG.4B (purple dashed region in FIG.4A) and the middle frame in FIG.4B (yellow shaded region in FIG.4A).
  • This differential signal also contains information on cardiac activity, as prominent S1 and S2 peaks of an SCG (1.5 s; green dashed region in the middle frame in FIG.4B).
  • FIGS.4C-4D compares RR and HR results extracted on the basis of normal and differential approaches. In the absence of body motions (e.g., sitting), the values are similar (blue shaded region in FIGS.4C-4D).
  • FIG.24 (A-C) shows Bland-Altman plots for RR (single sensor, FIG.24 (A-B); dual sensor, FIG.24 (C)).
  • results with IMU1 (red) and IMU2 (black) exhibit a mean difference of ⁇ 0.84 RPM (IMU1) and ⁇ 0.90 RPM (IMU2) and an SD of 8.72 RPM (IMU1) and 10.49 RPM (IMU2).
  • the differential results (blue) show a mean difference of 0.27 RPM and SD of 1.93 RPM.
  • results with IMU1 (red) and IMU2 (black) show a mean difference of ⁇ 2.23 BPM (IMU1) and ⁇ 4.12 BPM (IMU2) and an SD of 13.92 BPM (IMU1) and 13.18 (IMU2), and those with differential data (blue) show a mean difference of 0.01 BPM and an SD of 2.71 BPM (FIG.25).
  • the differential signal from dual sensing shows an improvement of 77 and 79% over RR and HR from single-sensing data, respectively.
  • Examples during Vigorous Activities in Sports Athletic competition, fitness training, manual labor, and related activities create daunting challenges for accurate measurements of RR and HR because of fast, dynamic, and highly variable large-amplitude accelerations of the body.
  • the dual-sensor platform offers powerful capabilities in these and other contexts.
  • FIG.5 highlights examples in cycling, playing basketball, and swimming.
  • both the single-sensing (IMU1) and dual-sensing (differential) data yield HR results that match those obtained with a reference device (General Electronics, Dash3000).
  • the RR values are also similar because of the limited effects of motion artifacts in this scenario.
  • differential sensing uniquely provides reliable measurements of HR and RR, as might be expected on the basis of controlled studies described previously (FIGS.5C- 5D). Further benefits appear during extreme motions (3.5 to 5 min in FIG.5D).
  • the water-tight encapsulation and internal nonvolatile memory allows use in aquatic sports, as illustrated during swimming (5 min in FIG.5E-5F).
  • the differential measurement approach is particularly valuable.
  • RR calculations that use the signal from a single IMU are dominated by responses associated with swimming strokes.
  • Our algorithm processes the results as outliers because of the large amplitudes of these accelerations, which are inconsistent with respiration, as shown in FIG. 5F.
  • the differential signal from the dual sensor greatly minimizes signals associated with swimming strokes, thereby yielding clear features associated with cycles of exhalation and inhalation and enabling calculations of the RR.
  • the differential signal yields accurate respiratory activity, although the patterns of breathing and swimming occur in the same frequency range, as shown in FIGS.5G-5H.
  • the differential signal shows clear features associated with exhalation/inspiration, well matched to the periodicity and the amplitude of controlled breathing. Examples during Vigorous Activities in Manual Labor Worker health represents another area of opportunity given the need to continuously monitor key cardiopulmonary parameters in hostile environments.
  • FIG.28 highlights examples of manual labor including hammering nails, carrying boxes, and shoveling dirt.
  • Data in FIG.29 (A) show that the differential signal exhibits clear features of cardiac activity, otherwise hidden by the strong, impulsive features associated with hammering. Similarly, respiratory features can be easily extracted even during large and irregular signatures of body movements in these cases, as shown in FIG.29 (B-C).
  • FIGS.6A-6D show a decreasing trend in cumulative cough count, along with the RR during this same interval, where the blue dots represent 5-min averages and the orange line shows the data after processing with Savitzky- Golay smoothing.
  • FIG.6C summarizes the HR, where the black dots and red line show similar averages and smoothed results, respectively.
  • FIG.6D presents the activity level (green bar) calculated by integrating the spectral power across a frequency range from 1 to 10 Hz and the estimated core body temperature (red line). Daytime corresponds to the time interval between 6 a.m. and 6 p.m., while other times are considered night. Average body temperature recorded from the first day (37.5°C) compared to the eighth recovery day (37.0°C) shows a decrease of 0.5°C. This recovery period includes a regimented and intense set of physical rehabilitation protocols. The ability track vital signs and key symptoms throughout could provide actionable clinical information on recovery and patient readiness to return home.
  • Thermal Insulating Foam A three-axis milling machine (Roland MDX 540) created an aluminum mold with a concave shape. Casting a liquid precursor to a polyurethane foam material (mixing ratio of A to B is 2:3; FlexFoam-iT! III, Smooth-On, USA) on the mold after coating its surface with a releasing agent (Ease Release 200, Smooth-On, USA) and then pressing a flat aluminum plate on top side produced insulation foams upon curing on a hot plate at 100°C for 30 min.
  • a releasing agent Ease Release 200, Smooth-On, USA
  • a reflective film (thermal blanket; Swiss Safe Products) attached to the flat bottom surface of the foam layer using a 5- ⁇ m-thick double-sided tape (No.5600, Nitto Denko Co., Japan) further improved the insulating properties.
  • the final step of the process involved a CO2 laser (Universal Laser System Inc.) to cut the perimeter of the material into the final geometry.
  • the thin Cu and PI films were modeled by composite shell elements (S4R).
  • the number of elements in the model was ⁇ 2 ⁇ 10 5 , and the minimal element size was 1 /8 of the width of the narrowest interconnects (100 ⁇ m).
  • the mesh convergence of the simulation was guaranteed for all cases.
  • 3D FEA Modeling for the Thermal Characteristics Transient heat transfer analysis determined the effects of thermal conduction and natural convection on the responses of the temperature sensors.
  • the tissue and internal sensor components were modeled by hexahedron elements (DC3D8).
  • the encapsulation layer was modeled using tetrahedron elements (DC3D4).
  • the number of elements in the model was ⁇ 6 ⁇ 10 5 , and mesh convergence of the simulation was ensured for all cases.
  • the boundary conditions included a constant temperature (T core ) at the bottom surface of the tissue layer and convection conditions with the ambient air (Tamb) at the free surfaces.
  • Image sequences were preprocessed by subtracting the background noise and enhancing the contrast.3D calibration exploited the structure-from-motion technique from multiple views. After removing effects of lens distortion, intrinsic parameters of a single camera were estimated using the checkboard calibration method. Extrinsic parameters of all four cameras, including 3D translation and rotation matrices, were obtained by using a sparse set of points matched across the views. Once all camera parameters were estimated, a dense set of fiducial points across multiviews were detected in a subpixel level and reconstructed in 3D coordinate.3D reconstructed fiducial points were tracked using the Hungarian algorithm and linked by performing a five-frame gap closing to produce long trajectories.
  • Training data included time series z-axis acceleration data with features associated with tapping, coughing, laughing, and throat clearing. Training of this classifying algorithm used 10 datasets from each class (subjects SP1 to SP4), as shown in FIG.32. Feature extraction used peak detection, spectral information, and spectrograms. The first step identified events associated with tapping, coughing, laughing, and throat clearing using adapted thresholds according to the input signal levels evaluated across sliding windows with widths of 0.5 s. Each extracted event was then aligned to the center of corresponding time frames to maximize the energy of the signal for postprocessing (first row of FIG.31 (C)) based on continuous wavelet transformations.
  • Resulting images within a 0.12-s window formed the basis for further analysis and classification (third row of FIG.31 (C)).
  • a binary tree architecture using a support vector machine (SVM) classified these extracted features into four activities, as shown in FIG.31 (D).
  • throat clearing activities were removed by negative values of the SVM1 hyperplane.
  • tapping activities were classified from SVM2 with a specific decision boundary (SVM2 result value: 2.5).
  • SVM3 separated the classes (coughing and laughing) with another decision boundary as described in FIG. 31 (E).
  • the core body temperature is determined from temperature variations through the thickness direction of the device.
  • a 1-D heat transfer model based on the device material layers (thickness ti and thermal conductivity k i listed in FIG. 14) was derived to estimate the core body temperature T core from the temperature difference between the IMU sensors and the ambient temperature T amb .
  • IMU1 and IMU2 is based on the equation where z denotes the coordinate along the thickness direction in FIG. 14 (A).
  • IMU 1 sensor can be expressed as
  • the temperature of the IMU1 sensor can be expressed as
  • FIG. 17 (A) shows that the temperature of IMU1 and IMU2 between 1-D analytical model and the 3-D FEA results agree well over the range of relevant heat convection coefficients h.
  • the simplified 1-D model can be used to determine an expression for T core by subtracting T IMU1 — T IMU2 AS
  • the ratios (B/A) and (D/C) are given below and depend h. If the device k, head, arm, etc.) then the skin/tissue thermal properties (i.e., thickness and thermal conductivity) would have to be adjusted accordingly (depending on the anatomy of the skin/tissue layers) for each location. Experiments show that the temperature of the battery changes by a negligible amount ( ⁇ 0.06 °C) during device operation (FIG.33). Analytical Modeling of Differential Accelerometry Although most of the capabilities in differential accelerometry arise from intrinsic differences in motions at the SN and SM, additional contributions can arise from details associated with the device layout. A schematic illustration of an analytical model that captures these structural differences is in FIG.20.
  • IMU1 located at the SN, is tied to the rigid platform (i.e. same displacement as equation (20)), considered as the chest wall.
  • Both IMU1 and IMU2 have a mass m.
  • the total height is ⁇ 8mm and the mass of accelerator is ⁇ 0.07g, so w should be larger than ⁇ 1000 s -1 .
  • the solution of A and ⁇ is not relevant in this case since the first term will decay very fast in a few seconds, thereby simplifying equation (23) to difference between IMU1 and IMU2. Meanwhile, b ⁇ 1, so b0 ⁇ 0 and the amplitude difference between IMU1 and IMU2 is very small.
  • This exemplary example presents, among other things, a low-profile, lightweight, flexible, and wireless sensor that intimately couples to the skin as a dual measurement interface to the SN and SM with modalities for differential sensing of temperature and MA signatures of body processes.
  • the results allow for measurements of a broad range of physiological parameters and activity behaviors that overcome a fundamental challenge in nearly every existing monitoring system: motion artifacts.
  • Comparisons with previous studies on the mechano- acoustic sensing method are presented in Table 1. Specific examples reported here include tracking of cardiac activity, respiratory activity, respiratory sounds, body temperature, and overall activity across a range of controlled settings and natural activities in sports, manual labor, and clinical medicine.
  • Examples include rehabilitation for patients with aphasia and/or dysphagia, where measurements of vocal activity and swallowing are possible during daily life, outside hospitals or rehabilitation clinics, of particular relevance to stroke survivors and patients with chronic obstructive pulmonary disease. Capabilities in tracking these processes without privacy concerns associated with microphone recordings and in a manner that is independent of ambient sounds represent key features of the approach.
  • multiaxial information including three-axis acceleration measurements, three-axis gyroscope data, and three-axis magnetometer information, suggests additional opportunities for these same platforms.
  • Examples include quantitative measurements of neck movements (FIG.30) for patients recovering from cervical spine surgery by using accurate vector data between IMU1 and IMU2, as well as full-body motion detection followed by full-body motion reconstruction for rehabilitation or early-stage atypical motion diagnosis for cerebral palsy.
  • Table 1 Comparisons with previous studies on the mechano-acoustic sensing method. Studies Liu et al Lee et al This invention r g Motion noise canceling Core bod temp estimation V) in) M
  • the foregoing description of the exemplary embodiments of the invention has been presented only for the purposes of illustration and description and is not intended to be exhaustive or to limit the invention to the precise forms disclosed. Many modifications and variations are possible in light of the above teaching.
  • Kim et al. Ballistocardiogram as proximal timing reference for pulse transit time measurement: Potential for cuffless blood pressure monitoring. IEEE Trans. Biomed. Eng.62, 2657-2664 (2015). [23].
  • W. Gao et al. Fully integrated wearable sensor arrays for multiplexed in situ perspiration analysis. Nature 529, 509-514 (2016). [24].
  • H. Lee et al. A graphene-based electrochemical device with thermoresponsive microneedles for diabetes monitoring and therapy. Nat. Nanotechnol.11, 566-572 (2016). [25].
  • Y. Yamamoto et al. Printed multifunctional flexible device with an integrated motion sensor for health care monitoring. Sci. Adv.2, e1601473 (2016). [26].
  • J.-T. Kim L. P. Chamorro, Lagrangian description of the unsteady flow induced by a single pulse of a jellyfish. Phys. Rev. Fluids 4, 064605 (2019). [49].
  • J.-T. Kim et al. On the dynamics of air bubbles in Rayleigh-Bénard convection. J. Fluid Mech.891, A7 (2020). [50].
  • I. Awolusi et al. Wearable technology for personalized construction safety monitoring and trending: Review of applicable devices. Autom. Constr.85, 96-106 (2016). [51].

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Abstract

La présente invention concerne un dispositif électronique pour mesurer des paramètres physiologiques d'un sujet vivant, comprenant au moins une première unité de mesure inertielle (UMI) et une seconde UMI, la première UMI et la seconde UMI étant synchronisées dans le temps et séparées spatialement et mécaniquement l'une de l'autre ; et une unité de micro-dispositif de commande (MCU) couplée électroniquement à la première UMI et à la seconde UMI pour le traitement de flux de données provenant de la première UMI et de la seconde UMI.
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