WO2022094451A1 - Détection mécano-acoustique avancée et applications de celle-ci - Google Patents

Détection mécano-acoustique avancée et applications de celle-ci Download PDF

Info

Publication number
WO2022094451A1
WO2022094451A1 PCT/US2021/057686 US2021057686W WO2022094451A1 WO 2022094451 A1 WO2022094451 A1 WO 2022094451A1 US 2021057686 W US2021057686 W US 2021057686W WO 2022094451 A1 WO2022094451 A1 WO 2022094451A1
Authority
WO
WIPO (PCT)
Prior art keywords
electronic device
sensor
imu
living subject
data
Prior art date
Application number
PCT/US2021/057686
Other languages
English (en)
Inventor
Shuai Xu
Hyoyoung Jeong
Jong Yoon Lee
Kun Hyuck LEE
John A. Rogers
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
Priority to CN202180083342.2A priority Critical patent/CN116782823A/zh
Priority to US18/034,893 priority patent/US20240000322A1/en
Priority to EP21887763.7A priority patent/EP4236776A4/fr
Publication of WO2022094451A1 publication Critical patent/WO2022094451A1/fr

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
    • A61B5/01—Measuring temperature of body parts ; Diagnostic temperature sensing, e.g. for malignant or inflamed tissue
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—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
    • A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
    • A61B5/1118—Determining activity level
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/42—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
  • 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 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.
  • 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 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 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.
  • 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.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.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.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
  • 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 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).
  • 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).
  • 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.
  • 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
  • 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).
  • 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.
  • 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 .
  • the temperature of the IMU1 sensor can be expressed as
  • 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.
  • 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].

Landscapes

  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Public Health (AREA)
  • Molecular Biology (AREA)
  • Physiology (AREA)
  • Veterinary Medicine (AREA)
  • Physics & Mathematics (AREA)
  • General Health & Medical Sciences (AREA)
  • Biophysics (AREA)
  • Pathology (AREA)
  • Biomedical Technology (AREA)
  • Heart & Thoracic Surgery (AREA)
  • Medical Informatics (AREA)
  • Animal Behavior & Ethology (AREA)
  • Surgery (AREA)
  • Cardiology (AREA)
  • Signal Processing (AREA)
  • Dentistry (AREA)
  • Oral & Maxillofacial Surgery (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Artificial Intelligence (AREA)
  • Psychiatry (AREA)
  • Pulmonology (AREA)
  • Measuring And Recording Apparatus For Diagnosis (AREA)
  • Measuring Pulse, Heart Rate, Blood Pressure Or Blood Flow (AREA)

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.
PCT/US2021/057686 2018-02-16 2021-11-02 Détection mécano-acoustique avancée et applications de celle-ci WO2022094451A1 (fr)

Priority Applications (3)

Application Number Priority Date Filing Date Title
CN202180083342.2A CN116782823A (zh) 2020-11-02 2021-11-02 先进机械-声学感测和其应用
US18/034,893 US20240000322A1 (en) 2018-02-16 2021-11-02 Advanced mechano-acoustic sensing and applications of same
EP21887763.7A EP4236776A4 (fr) 2020-11-02 2021-11-02 Détection mécano-acoustique avancée et applications de celle-ci

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US202063108514P 2020-11-02 2020-11-02
US63/108,514 2020-11-02

Publications (1)

Publication Number Publication Date
WO2022094451A1 true WO2022094451A1 (fr) 2022-05-05

Family

ID=81383306

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/US2021/057686 WO2022094451A1 (fr) 2018-02-16 2021-11-02 Détection mécano-acoustique avancée et applications de celle-ci

Country Status (3)

Country Link
EP (1) EP4236776A4 (fr)
CN (1) CN116782823A (fr)
WO (1) WO2022094451A1 (fr)

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20090030122A (ko) * 2007-09-19 2009-03-24 한국전기연구원 복합 생체신호 센서
US20180153423A1 (en) * 2015-04-21 2018-06-07 Shinano Kenshi Co., Ltd. Biological information reading device
KR20180086546A (ko) * 2017-01-22 2018-08-01 계명대학교 산학협력단 스트레스 측정을 위한 이어 헤드셋 장치 및 이를 이용한 스트레스 측정 방법
US20190069786A1 (en) * 2017-09-01 2019-03-07 Nestec Sa Heart rate detection device and related systems and methods
US20200129077A1 (en) * 2018-10-31 2020-04-30 Northwestern University Apparatus and method for non-invasively measuring blood pressure of mammal subject
US20200178906A1 (en) * 2017-06-14 2020-06-11 Heba BEVAN Medical devices

Family Cites Families (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
GB0706285D0 (en) * 2007-03-30 2007-05-09 You Care Ltd Precordial device
US10588565B2 (en) * 2010-04-22 2020-03-17 Leaf Healthcare, Inc. Calibrated systems, devices and methods for preventing, detecting, and treating pressure-induced ischemia, pressure ulcers, and other conditions
AT514017B1 (de) * 2013-02-22 2020-11-15 Dr Skrabal Falko Hämodynamisches EKG
WO2015128842A1 (fr) * 2014-02-27 2015-09-03 Technion Research & Development Foundation Ltd. Méthode, dispositif et système pour le suivi de la progression infraclinique et de la régression d'une insuffisance cardiaque
WO2018136462A1 (fr) * 2017-01-18 2018-07-26 Mc10, Inc. Stéthoscope numérique utilisant une suite de capteurs mécano-acoustiques
KR20210072105A (ko) * 2018-10-31 2021-06-16 노오쓰웨스턴 유니버시티 포유류 대상의 생리학적 파라미터를 비침습적으로 측정하기 위한 장치 및 방법 및 그 응용

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20090030122A (ko) * 2007-09-19 2009-03-24 한국전기연구원 복합 생체신호 센서
US20180153423A1 (en) * 2015-04-21 2018-06-07 Shinano Kenshi Co., Ltd. Biological information reading device
KR20180086546A (ko) * 2017-01-22 2018-08-01 계명대학교 산학협력단 스트레스 측정을 위한 이어 헤드셋 장치 및 이를 이용한 스트레스 측정 방법
US20200178906A1 (en) * 2017-06-14 2020-06-11 Heba BEVAN Medical devices
US20190069786A1 (en) * 2017-09-01 2019-03-07 Nestec Sa Heart rate detection device and related systems and methods
US20200129077A1 (en) * 2018-10-31 2020-04-30 Northwestern University Apparatus and method for non-invasively measuring blood pressure of mammal subject

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
See also references of EP4236776A4 *

Also Published As

Publication number Publication date
EP4236776A4 (fr) 2024-09-25
CN116782823A (zh) 2023-09-19
EP4236776A1 (fr) 2023-09-06

Similar Documents

Publication Publication Date Title
Lee et al. Mechano-acoustic sensing of physiological processes and body motions via a soft wireless device placed at the suprasternal notch
Lu et al. Wearable health devices in health care: narrative systematic review
Jeong et al. Differential cardiopulmonary monitoring system for artifact-canceled physiological tracking of athletes, workers, and COVID-19 patients
Nicolò et al. The importance of respiratory rate monitoring: From healthcare to sport and exercise
Nascimento et al. Sensors and systems for physical rehabilitation and health monitoring—A review
US20210113099A1 (en) Wireless medical sensors and methods
JP7330281B2 (ja) 哺乳動物対象の生理学的パラメータを非侵襲的に測定するための装置及び方法並びにその適用
Farooq et al. A comprehensive review on the prospects of next-generation wearable electronics for individualized health monitoring, assistive robotics, and communication
Sanei et al. Body sensor networking, design and algorithms
Zhou et al. Wearable continuous blood pressure monitoring devices based on pulse wave transit time and pulse arrival time: A review
Yoo et al. Wireless broadband acousto-mechanical sensing system for continuous physiological monitoring
Vitazkova et al. Advances in respiratory monitoring: a comprehensive review of wearable and remote technologies
Bansal Real-Time Data Acquisition in Human Physiology: Real-Time Acquisition, Processing, and Interpretation—A MATLAB-Based Approach
Yuan et al. Soft electronics for advanced infant monitoring
Zhou et al. Skin-interfacing wearable biosensors for smart health monitoring of infants and neonates
Roshan Fekr et al. Movement analysis of the chest compartments and a real-time quality feedback during breathing therapy
Kher et al. A comprehensive review on wearable health monitoring systems
US20240000322A1 (en) Advanced mechano-acoustic sensing and applications of same
WO2022094451A1 (fr) Détection mécano-acoustique avancée et applications de celle-ci
JP2024503532A (ja) 集計表示フィードへのデータのコンテキスト変換
Roudjane et al. Innovative wearable sensors based on hybrid materials for real-time breath monitoring
Cranny et al. Vital sign sensing technology
Sprowls et al. Integrated Sensing Systems for Monitoring Interrelated Physiological Parameters in Young and Aged Adults: A Pilot Study
Lymberis Advanced wearable sensors and systems enabling personal applications
Zabihi et al. Inertial and Flexible Resistive Sensor Data Fusion for Wearable Breath Recognition

Legal Events

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

Ref document number: 21887763

Country of ref document: EP

Kind code of ref document: A1

WWE Wipo information: entry into national phase

Ref document number: 18034893

Country of ref document: US

NENP Non-entry into the national phase

Ref country code: DE

ENP Entry into the national phase

Ref document number: 2021887763

Country of ref document: EP

Effective date: 20230602

WWE Wipo information: entry into national phase

Ref document number: 202180083342.2

Country of ref document: CN