CN116782823A

CN116782823A - Advanced mechano-acoustic sensing and applications thereof

Info

Publication number: CN116782823A
Application number: CN202180083342.2A
Authority: CN
Inventors: 徐帅; 郑孝英; 李钟尹; 李坤赫; 约翰·A.·罗杰斯
Original assignee: NORTHWEST UNIVERSITY
Current assignee: NORTHWEST UNIVERSITY
Priority date: 2020-11-02
Filing date: 2021-11-02
Publication date: 2023-09-19
Also published as: EP4236776A1; WO2022094451A1

Abstract

The application discloses an electronic device for measuring a physiological parameter of a living subject, the electronic device comprising: at least a first Inertial Measurement Unit (IMU) and a second IMU, the first IMU and the second IMU being time synchronized with each other 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 data streams from the first IMU and the second IMU.

Description

Advanced mechano-acoustic sensing and applications thereof

Statement of rights under federally sponsored research

The application was completed with government support under 75a50119C00043 awarded by the preparation and response assistant secretary office (the Office of the Assistant Secretary for Preparedness and Response) and AG062023 and AG060812 awarded by the national institutes of health (the National Institutes ofHealth). The government has certain rights in this application.

Cross-reference to related patent applications

The present application claims priority and benefit from U.S. provisional patent application Ser. No. 63/108,514, filed 11/2/2020.

The present application also continues in part with U.S. patent application Ser. No. 16/970,023, filed 8/14/2020, which is a national phase entry of PCT patent application Ser. No. PCT/US2019/018318, filed 2/15/2019, which itself claims priority and benefit from U.S. provisional patent application Ser. No. 62/710,324, filed 2/16/2018, 62/631,692, filed 2/17/2018, and 62/753,203, filed 10/31/2018.

Each of the above-identified applications is incorporated by reference herein in its entirety.

Technical Field

The present invention relates generally to biosensors and more particularly to advanced mechanical-acoustic sensing systems and applications thereof.

Background

The background description provided herein is for the purpose of generally presenting the context of the disclosure. The subject matter discussed in the background section of the present invention should not be assumed to be prior art merely due to the mention of the subject matter in the background section of the present invention. Similarly, the problems mentioned in or associated with the subject matter of the background section of this invention should not be assumed to have been previously identified in the prior art. The subject matter in the background section of this invention represents only a different approach, which itself may also be an invention. Work of the presently named inventors, to the extent it is described in this background section, as well as aspects of the description that may not otherwise qualify as prior art at the time of filing, are neither expressly nor impliedly admitted as prior art against the present disclosure.

The advent of wearable technologies capable of multi-mode clinical-level monitoring of physiological health has increased the need for sensors, systems, and data analysis methods that enable reliable, continuous operation during natural daily activities. Skin-mounted technology provides a very superior measurement capability compared to conventional devices that are loosely coupled to the wrist due to their durable, intimate interface with the body. This mode of operation may support a range of clinical standard diagnostic evaluations, such as evaluations based on electrocardiography, photoplethysmography, arterial tonometry, and the like. A recent set of important capabilities comes from the wide bandwidth measurement of fine movements and vibrations of the skin surface (i.e. mechanical-acoustic (MA) response) caused by the activity of internal organs and accelerations due to the overall movement of the body. Skin interface devices for such purposes use accurate high bandwidth accelerometers based on microelectromechanical systems technology in a layout that optimizes sensitivity to motion of the skin surface over a wide frequency range from near zero to several kilohertz. The generated data reflects not only the overall motion of the body, as obtained by conventional wearable devices, but also features from a wide range of body sounds, as obtained by digital stethoscopes, but is not affected by ambient sounds. Additional information appears in the frequency range between these limits. When mounted on the neck or chest, the recordings enable detailed assessment of heart activity from heart movements and pulsatile flow of blood through the near-surface arteries, respiratory cycles from chest wall movements, respiratory sounds from airflow through the lungs and trachea, swallowing behaviour from laryngeal movement and oesophageal movements, vocal patterns from vocal cord activation, and movement and orientation changes of the core body. The unique features in the temporal and spectral characteristics of these processes create insight into physical activity and health status in a seamless manner through a large number of conventional (e.g., heart Rate (HR)) and non-conventional (e.g., cough frequency) metrics, without privacy concerns arising from the use of microphones or other recording devices.

By these mechanisms, only a single device in a sealed, waterproof package that needs to be mechanically coupled to the skin can produce a vast range of health-related information. An important consideration is that the different ranges of the MA signal contribute to a single time-series data stream in a time-overlapping manner. Advanced data filtering and analysis methods can separate and quantify different feature events based on unique temporal and spectral features, but the methods do not operate reliably in many scenarios of practical interest. Special challenges arise when different activities with similar spectral content occur simultaneously. These situations render the digital signal processing method ineffective. For example, the respiration rate cannot be accurately determined while running. Related types of motion artifact are fundamental limitations for both wrist-mounted consumer wearable devices and clinical-grade wired monitoring systems.

Accordingly, there is a heretofore unaddressed need in the art to address the aforementioned deficiencies and inadequacies.

Disclosure of Invention

In certain aspects, the present invention discloses a novel approach that overcomes the above limitations at the hardware level through advanced concepts in system design and optimized selection in anatomical installation locations, without requiring complex and often ineffective digital signal processing strategies. In some embodiments, the method utilizes a pair of time-synchronized high bandwidth accelerometers (inertial measurement units (IMUs)) at opposite ends of a skin-interfacing device that positions an IMU of the IMUs at an Suprasternal Notch (SN) and another IMU at a manubrium (SM). Differences in the movement of the skin associated with cardiac and respiratory activity between these regions result in differences in the signals captured by these IMUs. In contrast, the overall movement of the neck and body core produces nearly identical responses. Thus, simple differential measurements cleanly eliminate common mode features, thereby separating signals associated with heart lung and related processes from signals generated due to body movement. An additional benefit of this architecture is that the temperature sensors integrated in these IMUs can be used in a similar differential manner to produce an estimate of core body temperature, which is largely immune to the environment. Here, careful selection of the thermal aspect of the device layout, rather than the inherent anatomical gradient, will produce the necessary differential response.

In one aspect, the invention relates to an electronic device for measuring a physiological parameter of a living subject, the electronic device comprising: at least a first IMU and a second IMU, the first IMU and the second IMU being time synchronized with each other 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 data streams from the first IMU and the second IMU.

In one embodiment, the first IMU is configured to measure data comprising a first signal and a second signal related to a physiological signal of the living subject, and the second IMU is configured to measure data comprising at least the second signal. The signal strength of the first signal measured by the first IMU is greater than the signal strength of the second signal measured by the first IMU.

In one embodiment, the data measured by the first IMU and the second IMU are processed such that subtracting the second signal measured by the second sensor from the second signal measured by the first sensor produces a stronger first signal that is the signal of interest.

In one embodiment, the second signal is related to at least one of an environment, a motion, and a vibration.

In one embodiment, the data measured by the second IMU includes the first signal and the second signal.

In one embodiment, a signal-to-noise ratio (SNR) of signals measured by the first IMU and the second IMU together is lower than a first SNR of signals measured by the first IMU alone or a second SNR of signals measured by the second IMU alone.

In one embodiment, both the first IMU and the second IMU are in operable mechanical communication with the skin of the living subject.

In one embodiment, one of the first IMU and the second IMU is in operative direct mechanical communication with the skin of the living subject for sensing physiological signals of the body, and the other of the first IMU and the second IMU is in operative indirect mechanical communication with the skin of the living subject.

In one embodiment, the first IMU and the second IMU are in operative direct mechanical communication with the skin of the living subject.

In one embodiment, one of the first IMU and the second IMU is separate from the remaining rigid components of the electronic device.

In one embodiment, the electronic device further comprises at least a first and a second thermal sensing unit, wherein one of the first and second thermal sensing units is thermally isolated from the surrounding environment and configured to measure the body temperature of the living subject, and the other of the first and second thermal sensing units is configured to measure the ambient temperature.

In one embodiment, each of the first and second thermal sensing units is embedded in a respective one of the first and second IMUs.

In one embodiment, the electronic device is configured to measure a range of physiological information from the activity of the cardiopulmonary system and movement of the nucleosome to a collection of various processes related to respiration, speech, swallowing, wheezing, coughing and sneezing across the chest, esophagus, pharynx and mouth.

In one embodiment, the electronic device is configured to separate signals associated with the cardiopulmonary system and related processes from signals generated due to body movement.

In one embodiment, the electronic device is configured to spatiotemporal map movement of skin at this region of the anatomy to which the electronic device is attached during cardiac and respiratory activity.

In one embodiment, the electronic device is configured to continuously measure temperature, heart Rate (HR), respiration Rate (RR), activity level, and body orientation across a range of strenuous activities and conditions.

In one embodiment, the electronic device is configured to monitor key symptoms of a patient suffering from a covd-19 infection to track progression of recovery and response to therapy in a hospital and/or home.

In one embodiment, the electronic device is configured to measure any respiratory or exercise related digital biomarker associated with a cough, a swallow, and/or a particular exercise related activity.

In one embodiment, the electronic device is configured to evaluate cough when the living subject is moving or not moving, and/or measure muscle movement when the living subject is moving.

In one embodiment, the electronic device further comprises a two-way wireless communication system electronically coupled to the electronic device and configured to transmit an output signal from the electronic device to an external device.

In one embodiment, the external device is a mobile device, a computer, or a cloud service.

In one embodiment, the two-way wireless communication system is further configured to deliver commands from the external device to the electronic device.

In one embodiment, the two-way wireless communication system includes a controller that communicates wirelessly using at least one of Near Field Communication (NFC), wi-Fi/Internet, bluetooth Low Energy (BLE), and cellular communication protocols.

In one embodiment, the electronic device further includes a custom application having a user interface disposed in the external device to allow a user to configure and operate the electronic device for data collection, data transmission, data storage and analysis, wireless charging, and user condition monitoring.

In one embodiment, the custom application is configured to allow simultaneous time-synchronized operation of a plurality of the electronic devices.

In one embodiment, the electronic device further comprises a power module coupled to the first IMU, the second IMU, and the MCU for providing power to the first IMU, the second IMU, and the MCU.

In one embodiment, the power module includes at least one battery for providing the power. In one embodiment, the battery is a rechargeable battery.

In one embodiment, the power module further comprises a wireless charging module for wirelessly charging the rechargeable battery.

In one embodiment, the power module further includes a fault protection element that includes a short circuit protection component or circuit to avoid battery faults.

In one embodiment, the second IMU is placed in a manner that the second IMU is bent and folded over the battery.

In one embodiment, the electronic device further includes a flexible printed circuit board (fPCB) having flexible and stretchable interconnects electrically connected to electronic components including the first IMU, the second IMU, and the MCU, and the power module.

In one embodiment, the electronic device further comprises an elastomeric encapsulant layer at least partially surrounding the electronic component and the flexible and stretchable interconnect to form a tissue facing surface and an environmental facing surface attached to the living subject, wherein the tissue facing surface is configured to conform to a skin surface of the living subject.

In one embodiment, the encapsulation layer is formed of a flame retardant material.

In one embodiment, the elastomeric encapsulant layer is a waterproof and biocompatible silicone shell.

In one embodiment, the electronic device further comprises a biocompatible hydrogel adhesive for attaching the electronic device on a respective area of the living subject, wherein the biocompatible hydrogel adhesive is adapted such that signals from the living subject are operatively conducted to the first IMU and the second IMU.

In one embodiment, the electronic device is flexible and conformable to skin with a particular geometric polarity for installation in an anatomical location of interest of the living subject.

In one embodiment, the electronic device is wearable, torsionally stretchable and/or bendable.

In another aspect, the invention relates to an electronic device for measuring a physiological parameter of a living subject, the electronic device comprising a sensor network comprising a plurality of sensor units operably deployed on the skin of the living subject, the plurality of sensor units being time synchronized with each other and spatially and mechanically separated from each other; and an MCU electronically coupled to the plurality of sensor units for processing the data streams from the plurality of sensor units.

In one embodiment, the plurality of sensor units are configured to measure the same physiological parameter or different physiological parameters.

In one embodiment, each sensor unit of the plurality of sensor units comprises at least a first sensor and a second sensor, the first sensor and the second sensor being time synchronized with each other and spatially and mechanically separated from each other.

In one embodiment, for each sensor unit, the first sensor is configured to measure data comprising a first signal and a second signal related to a physiological signal of the living subject, and the second sensor is configured to measure data comprising at least the second signal. The signal strength of the first signal measured by the first sensor is greater than the signal strength of the second signal measured by the first sensor.

In one embodiment, the data measured by the first sensor and the second sensor of the sensor unit is processed such that subtracting the second signal measured by the second sensor from the second signal measured by the first sensor results in a stronger first signal, the stronger first signal being a signal of interest.

In one embodiment, each of the first sensor and the second sensor includes the IMU, a thermal sensor, a pressure sensor, and/or an optical sensor.

In one embodiment, each of the first sensor and the second sensor includes the IMU.

In one embodiment, the electronic device further comprises a plurality of thermal sensing units.

In one embodiment, each thermal sensing unit is embedded in a respective IMU.

In one embodiment, the MCU is operable to receive input from synchronized outputs of a plurality of thermal sensor units having at least one thermal sensing unit for an ambient environment and at least one thermal sensing unit in direct thermal communication with the body thermally isolated from the ambient environment with built-in sensor thermal isolation material.

In one embodiment, the electronic device is configured to automatically switch modes of operation including at least a first mode when the living subject is at rest and a second mode when the living subject is in high motion.

In one embodiment, the two-way wireless communication system includes a controller that communicates wirelessly using at least one of NFC, wi-Fi/internet, bluetooth, BLE, and cellular communication protocols.

In one embodiment, the custom application is configured to allow time-synchronized operation of multiple of the sensor networks simultaneously.

In one embodiment, the electronic device further comprises a power module coupled to the sensor network for providing power to the sensor network.

In yet another aspect, the invention relates to an electronic device for measuring a physiological parameter of a living subject, the electronic device comprising a first sensor adapted to detect a first set of data related to the living subject and a second set of data different from the first set of data; and a second sensor for detecting a third set of data substantially similar to the second set of data. In operation, the first sensor and the second sensor are time synchronized to allow the third set of data from the second sensor to be used to substantially cancel the second set of data from the first sensor.

In one embodiment, the first sensor and the second sensor are spatially and mechanically separated from each other.

In one embodiment, the first sensor and the second sensor are spaced apart by more than zero and less than a predetermined distance.

In one embodiment, each of the first sensor and the second sensor comprises an IMU, a thermal sensor, a pressure sensor, or an optical sensor.

In one embodiment, the first set of data is a physiological signal of the living subject and the second set of data is an environmental signal at the first sensor.

In one embodiment, the third set of data is an environmental signal at the second sensor.

In one embodiment, both the first sensor and the second sensor are in operable mechanical communication with the skin of the living subject.

In one embodiment, the first sensor is operable to be in direct mechanical communication with the skin of the living subject for sensing physiological signals from the body, and the second sensor is operable to be in indirect mechanical communication with the skin of the living subject.

In one embodiment, the first sensor and the second sensor are in operative direct mechanical communication with the skin of the living subject for sensing physiological signals from the body to assess pulse transit time.

In a further aspect, the invention relates to an electronic device for measuring a physiological parameter of a living subject, the electronic device comprising a first sensor adapted to detect a first set of data related to the living subject and a second set of data different from the first set of data; and a second sensor for detecting a third set of data substantially similar to the second set 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 a region of the living subject where a physiological signal of the living subject is 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.

In one embodiment, the second sensor is positioned above the first sensor.

In one embodiment, the second sensor is located remotely 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 set of data is a physiological signal of the living subject and the second set of data is a signal related to the environment, motion, and/or vibration at the first sensor.

In one embodiment, the third set of data is a signal related to the environment, motion, and/or vibration at the second sensor.

In one embodiment, both the first sensor and the second sensor are in operative direct mechanical communication with the skin of the living subject for sensing physiological signals from the body to assess pulse transit time.

These and other aspects of the present invention will become apparent from the following description of the preferred embodiments, which is to be read in connection with the accompanying drawings, although variations and modifications may be affected therein without departing from the spirit and scope of the novel concepts of the disclosure.

Drawings

The accompanying drawings illustrate one or more embodiments of the invention and, together with the written description, serve to explain the principles of the invention. Wherever possible, the same reference numbers are used throughout the drawings to refer to the same or like elements of an embodiment.

Figures 1A-1F illustrate images, schematic diagrams, functional flow diagrams, and mechanical modeling results for a wireless, skin interface device designed for dual MA measurements at SN and SM, according to an embodiment of the invention. Fig. 1A: an image of a device mounted on the bottom of the neck, wherein one end of the device is located at SN and the other end is located at SM. Fig. 1B: exploded views of the active components, interconnection scheme, and housing architecture are intended. Fig. 1C: next to the image of the device of the us coin (diameter, 24.26 mm). Fig. 1D: images of the device during various mechanical deformations: twist angle 90 ° (left), uniaxial stretching 45% (middle) and bend angle 180 ° (right). Fig. 1E: finite element modeling of the mechanics for deformation in fig. 1D. The contour plot shows the maximum principal strain for torsion (left), tension (middle) and bending (right) in the metal layer of the serpentine interconnect. Fig. 1F: a block diagram of the system operation. The tablet computer provides an interface for operating the device, wirelessly downloading data from the device and transmitting the data to the cloud server over the cellular network. Processing on the cloud platform produces vital signals (HR, respiration, and body temperature) and other metrics of interest (cough count and physical activity).

FIGS. 2A-2G illustrate dual sensing platforms for temperature differential and MA sensing according to embodiments of the invention. Fig. 2A: exploded view and fig. 2B: the cross-section of the device is schematic. Fig. 2C: a side view of the complete device next to the us coin. Fig. 2D: finite element results of temperature distribution in the skin and outside the device for 37 ℃ and 22 ℃ for skin temperature and ambient temperature, respectivelyThe medium convection coefficient is 10w m- ² K- ¹ . Cross-sectional profile of temperature along the a-B axis (inset). Fig. 2E: temperature distribution along the a-B cross section for different ambient temperatures and convection coefficients. Fig. 2F: the temperature difference measured using the temperature sensors in IMU1 and IMU 2. (D) to (F) correspond to the case where the core body temperature is 37 ℃. Fig. 2G: representative results determined as the subject moves through the room at various ambient temperatures. Double temperature (first row), temperature difference (second row), and core body temperature calibrated and measured (third row).

Figures 3A-3H illustrate the distribution of displacements over the neck and surrounding areas determined by the 3D-PTV during natural respiratory and cardiac activity, focusing on SN and SM, according to an embodiment of the invention. Fig. 3A: a 3D vector of displacement superimposed on the neck image and a contour field. Fig. 3B: fig. 3A is a 3D view. The color represents the velocity w along the z-axis during the cardiac cycle. Fig. 3C: during breath hold, the displacement AZ along the Z-axis at SN and SM highlights heart activity as a function of time. Fig. 3D: differential displacement between SN and SM determined from the data in fig. 3C. Fig. 3E: the color profile of AZ at the peak of the cardiac cycle is highlighted by the blue arrow in fig. 3D. . Fig. 3F: during respiration and slight body movements, AZ is a function of time at SN and SM. Fig. 3G: differential displacement between SM and SN determined from the data in fig. 3F. Fig. 3H: the color profile of AZ at the peak of inhalation is highlighted by the blue arrow in fig. 3G.

Fig. 4A-4D illustrate representative data collected during various non-walking motions and measurements of a controlled RR and normal HR in accordance with an embodiment of the invention. Fig. 4A: the subject sits quietly for 7 minutes, walks for 14 minutes at rest intervals, runs for 8 minutes at rest intervals, and jumps for 7 minutes at rest intervals under a controlled RR (6 to 35 RPM). Fig. 4B: an enlarged view of the walking and running signals from (a) highlighting baseline fluctuations associated with respiration. The right-most green outline box is a further enlarged view of the data from the middle box highlighting the heart activities S1 and S2. Fig. 4C: single accelerometer data (black dots) yields reliable RR values when the subject sits still. During walking exercise, single accelerometer data produces unreliable RR values. The differential signal (blue dot) produces an accurate respiration rate, consistent with the reference true value (green triangle). The red arrow represents the time frame of fig. 4B. Fig. 4D: the single accelerometer data reliably provides HR while the subject is sitting still. During walking motion, the single accelerometer data (black dots) yields unreliable values compared to the values from the differential signal (blue dots) and the reference true value (green triangles). The signal associated with the tap between transitions results in outliers. The red arrow represents the time frame of fig. 4B.

Figures 5A-5H illustrate tracking of cardiopulmonary activity during intense physical activity according to embodiments of the invention. Fig. 5A: image of dual sensing device at SN/SM along with for SpO ₂ And a reference device for electrocardiographic recording and a thermocouple for oral and ambient temperature measurements while riding. Fig. 5B: RR and HR comparisons determined by double sensing (blue squares) and single sensing (red circles) and reference device (green triangles, used only for HR) were made while riding for 24 minutes. Fig. 5C: the dual sensing device images on the SN/SM at the time of basketball play. Fig. 5D: comparison of RR and HR determined from dual and single sense data at 11 minutes of basketball play. Fig. 5E: the dual sensing device images on SN/SM while swimming. Fig. 5F: comparison of RR and HR as determined by dual and single sense data at 5 minutes of swimming. Fig. 5G: representative z-axis acceleration data acquired from the dual sensing device during swimming. Acceleration measured from IMU1 (red), IMU2 (black), calculated differential signal (blue), and baseline of differential signal (light blue). Fig. 5H: amplified data associated with the differential signal (blue) from the area highlighted by the green box of fig. 5G and its baseline (light blue).

Fig. 6A-6D illustrate data collected from a covd-19 patient in the form of cough counts, RR, HR, activity levels, and estimated core body temperature, according to an embodiment of the invention. Fig. 6A: changes in cough frequency from the patient when recovered over a period of 8 days. The first group was measured from 1 pm to 7 pm on the first day. The second group was measured from 8 am to 8 pm the next day. The third group was measured from 1 pm to 9 pm on the fourth day. The fourth group was measured at 9 am to 8 pm on the seventh day, and the fifth group was measured at 8 am to 8 pm on the eighth day. The purple line shows the cumulative number of coughs. Fig. 6B: changes in respiratory rate and results from Savitzky-Golay smoothing (orange line). Fig. 6C: the change in HR and the results from Savitzky-Golay smoothing (red line). Fig. 6D: activity levels (green bars) and estimated core body temperature (red) during the day (yellow shaded area) and night (blue shaded area), a.u., arbitrary units.

Fig. 7 shows the device during CDC directed cleaning and disinfection using 70% alcohol.

Fig. 8 shows a layout of a flexible PCB for a dual sensing device according to an embodiment of the present invention. The red dashed line shows the folding plane and the 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 measurement according to an embodiment of the present invention.

FIG. 10 illustrates a dual sensing system state diagram according to an embodiment of the invention.

Fig. 11 shows the temperature (T _core Temperature distribution of IMU1 and IMU2 along a-B cross section at =37 ℃, 380C, 39 ℃, 40 ℃).

FIG. 12 shows the temperature and convection coefficients (T _core Temperature difference between IMU1 and IMU2 at =37 ℃, 38 ℃, 39 ℃, 40 ℃).

Fig. 13 shows the temperature of the fluid at different ambient temperatures (T _amb :18 ℃ to 24 ℃ and convection coefficient (5W/m) ² K to 30W/m ² K) Next, IMU1 and IMU2 are temperature color mapped along the a-B cross-section.

FIG. 14 illustrates a 1-D heat transfer model according to an embodiment of the invention. (A) illustrations of heat transfer models. (B) thickness and thermal conductivity of each material layer.

Fig. 15 shows representative dual sensing data collected from a subject during movement within a room at various ambient temperatures. Temperature differences (lines) and percent differences (bars) between the reference temperature measurement data (oral thermocouples) and the estimated core body temperature (dual temperature sensing).

Fig. 16 shows a branchy-ottman plot (Bland-Altman plot) of the temperature difference between the oral thermocouple and IMU1 (a) (n=299 data points from 1 subject) and the calibrated temperature of the temperature difference from IMU1 and IMU2 (B) (n=359 data points from 1 subject).

FIG. 17 shows temperature results from a 1-D analytical model and a 3-D FEA model. (A) IMU1 and IMU2 temperatures from the 1-D analytical model and the 3-D FEA model. (B) At core temperature (T _core ) Maintained at 36.3℃by the temperature of the IMU1, IMU2, the difference and the temperature of the environment (T _amb ) The thermal convection coefficient (h.apprxeq.10W/(m) ² k) A determined analysis result.

Fig. 18 shows the 3D-PTV measurement results. Graphical representation of (a) photographs and (B) experimental setup. (C) The velocity along the z-axis w versus time at SN (IMU 1 position) and SM (IMU 2 position). (D) Starting from the green point in (C), the color contour of w at t=0.785 seconds (local maximum speed) of (C). (E) Displacement along the y-axis AY during respiration versus time at SN and SM. (F) (E) differential displacement IMU2-IMU1 along y-axis AY between SM and SN. Photo signature: jin-Tae Kim, university of northwest (Northwestern University).

Fig. 19 shows dual sensing data collected during 3D-PTV measurement. (A) Acceleration along the z-axis at SN and SM versus time during respiration. (B) velocity calculated along the z-axis of (A). (C) displacement calculated along the z-axis of (A). (D) differential displacement: SM-SN of (C).

FIG. 20 shows a simplified 1-D analytical model of differential accelerometer. (A) Schematic of a simplified 1-D analytical model for heart activity. The displacement is applied to a rigid platform, causing the 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 having a stiffness k and a damper having a damping ratio ζ. (B) Analysis of z-axis displacement from IMU1 and IMU2 during cardiac cycles without respiratory activity. (C) Differential displacement between IMU1 and IMU2 determined from the data in (B). (D) Differential displacement between IMU1 and IMU2 is determined from data during respiratory activity.

Fig. 21 shows data collected from different body orientations with both encapsulated and unencapsulated devices. (A) - (D) measured signals from the unpackaged dual sensing devices. (A) Under normal and breath-hold conditions, the subject sits quietly for 45 seconds, reclines for 70 seconds, and leans forward for 35 seconds. (B) An enlarged view of cardiac and respiratory activity from (a). (C) flipped dual sense orientation. IMU1 is placed on SM and IMU2 is placed on SN. Under normal and breath-hold conditions, the subject sits quietly for 20 seconds, reclines for 25 seconds, and leans forward for 30 seconds. (D) An enlarged view of cardiac and respiratory activity from (C). (E) - (H) measured signals from the packaged dual sensing means. (E) Under normal and breath-hold conditions, the subject sits quietly for 20 seconds, reclines at two different angles for 45 seconds, and leans forward for 20 seconds. (F) An enlarged view of cardiac and respiratory activity from (E). (G) flipped dual sense orientation. IMU1 is placed on SM and IMU2 is placed on SN. Under normal and breath-hold conditions, the subject sits quietly for 20 seconds, reclines at two different angles for 45 seconds, and leans forward for 20 seconds. (H) An enlarged view of cardiac and respiratory activity from (G).

Fig. 22 shows an algorithm for determining the respiration rate from the differential signal. (a) a block diagram of a signal processing flow in the frequency domain. (B) differential data derived from IMU1 and IMU 2. The walk-related pulse signals in IMU1 (red) and IMU2 (black) are largely absent from the differential data (blue). And (C) the differential data in (B). And (D) Fourier transform of (C). When the respiratory behaviour is stable and constant, only one high energy frequency occurs in the range of 6-60 rpm. (E) differential data for various respiratory rates. And (F) (E) Fourier transform. Five high energy frequencies appear in these data, three of which have more than 50% of the maximum energy. According to the algorithm in (a), the respiration rate corresponds to its weighted average.

Fig. 23 shows an algorithm for determining heart rate from differential signals. (a) a block diagram of a signal processing flow in the frequency domain. (B) differential data derived from IMU1 and IMU 2. (C) The bandpass filtered signal of (B), wherein the cutoff frequency is 45-170bpm. (D) using an exponential function to detect the envelope. Fourier transform of the envelope data in (E) (D).

Fig. 24 shows a brancher-ottman plot of the difference between the single sensed Respiration Rate (RR) from IMU1 (a), IMU2 (B) and the RR of the differential signal from IMU1 and IMU2 (C) (n=36 data points from 1 subject).

Fig. 25 shows a brancher-ottman plot of the difference between the Heart Rate (HR) from single sensing with IMU1 (a), IMU2 (B) and the HR of the differential signal from IMU1 and IMU2 (C) (n=39 data points from 1 subject).

Fig. 26 shows a measurement setup for stationary bicycle riding. (A) A subject on a bicycle wearing a sensor on SN/SM. (B) An enlarged image of the area highlighted by the orange box in (a).

Fig. 27 shows measured data from stationary bicycle riding. (A) Z-axis acceleration at 24 minutes on stationary bicycle. (B) The dual temperature value and the reference temperature value when riding the stationary bicycle.

Fig. 28 shows measured data during push-ups with controlled breathing rate. (A) Z-axis acceleration when performing push-ups under controlled respiratory cycles. (B) baseline of z-axis acceleration.

Fig. 29 shows measured data during hammering of nails, carrying of boxes, and shoveling. (A) Z-axis acceleration when hammering nails. And (B) Z-axis acceleration during lifting, carrying and placing of the box. And (C) Z-axis acceleration during soil shoveling.

Figure 30 shows motion artifacts from local movements. Local movements caused by neck movements. 3-axis acceleration data from IMU1 on SN.

Fig. 31 shows feature classification using a Support Vector Machine (SVM). (A) A flow chart of events is extracted from raw data using an adaptive threshold. (B) Acceleration data recorded during 50 seconds with various activities including tapping, coughing, laughing, and oropharynx. The blue line is a time series result of acceleration along the Z axis. The solid orange line is the envelope of the signal. The yellow line is an adaptive threshold for detecting a particular feature. The red dot is the center of the detected event. (C) Samples extracted after peak detection (line 1), FFT (line 2) and spectrogram (line 3) of cough, throat clearing, laugh and tap. (D) Binary tree architecture designs with SVMs for classifying these activities. (E) a plurality of SVM classification results. Classification result 1 after SVM1 (left): negative values of clearing pharynx (yellow x). Classification results 2 and 3 after SVM2 and SVM 3: classified taps (brown triangles) after SVM 2. Cough (red circle) and laugh (blue inverted triangle) classification after SVM 3.

FIG. 32 shows a dataset for developing a classifier. (A) Data including cough, laugh, tap, and oropharynx from 8 subjects. 10 data from each category in group 1 (SP 1 to SP 4) are used for the classifier. (B) Cough detection accuracy for each subject in group 1 and group 2.

Fig. 33 shows device temperature monitoring. (A) The experimental setup for monitoring the temperature of the device focused on the battery. (B) Battery temperature measurement screen after 8 minutes of operation at room temperature. (C) variation in battery temperature during this period.

Fig. 34A-34C schematically illustrate an electronic device according to various embodiments of the invention.

Detailed Description

The present invention now will be described more fully hereinafter with reference to the accompanying drawings, in which exemplary embodiments of the invention are shown. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art. Like numbers refer to like elements throughout.

The terms used in the present specification generally have their ordinary meanings in the art within the context of the present invention and in the specific context of the use of each term. Certain terms used to describe the invention are discussed below or elsewhere in this specification to provide additional guidance to the practitioner regarding the description of the invention. For convenience, certain terms may be highlighted, for example, using italics and/or quotation marks. The use of highlighting has no effect on the scope and meaning of a term; the scope and meaning of the terms are the same in the same context, whether or not the terms are highlighted. It should be understood that the same thing can be described in more than one way. Thus, alternative language and synonyms may be used for any one or more of the terms discussed herein, and no special significance should be added to the terms whether or not they are described or discussed in detail herein. Synonyms for certain terms are provided. The recitation of one or more synonyms does not exclude the use of other synonyms. The use of examples anywhere in this specification (including examples of any terms discussed herein) is illustrative only, and in no way limits the scope and meaning of the invention or any exemplified terms. As such, the present invention is not limited to the respective embodiments given in the present specification.

Those of ordinary skill in the art will appreciate that starting materials, biological materials, reagents, synthetic methods, purification methods, analytical methods, assay methods, and biological methods other than those specifically exemplified may be used to practice the present invention without resort to undue experimentation. All art-known functional equivalents of any such materials and methods are intended to be included in the present invention. The terms and expressions which have been employed are used as terms of description and not of limitation, and there is no intention in the use of such terms and expressions of excluding any equivalents of the features shown and described or portions thereof, but it is recognized that various modifications are possible within the scope of the invention claimed. Accordingly, it should be understood that although the present invention has been specifically disclosed by preferred embodiments and optional features, modification and variation of the concepts herein disclosed may be resorted to by those skilled in the art, and that such modifications and variations are considered to be within the scope of this invention as defined by the appended claims.

Whenever a range is given in the specification, such as a temperature range, a time range or a composition or concentration range, all intermediate ranges and subranges as well as all individual values included within the given range are intended to be included in the present invention. It should be understood that any subrange or single value contained within a range or subrange in the description herein may be excluded from the claims herein.

It should be understood that as used in the specification herein and throughout the claims that follow, the meaning of "a," "an," and "the" includes plural referents unless the context clearly dictates otherwise. Thus, for example, reference to "a cell" includes a plurality of such cells and equivalents thereof known to those skilled in the art. Also, the terms "a" (or "an"), "one or more" and "at least one" can be used interchangeably herein. It should also be noted that the terms "comprising," "including," and "having" are used interchangeably.

It will be understood that when an element is referred to as being "on," "attached to," "connected to," "coupled to," "contacting" another element, etc., it can be directly on, attached to, connected to, coupled to, contacting the other element or intervening elements may be present. In contrast, when an element is referred to as being "directly on," "directly attached to," "directly connected to," "directly coupled to," or "directly contacting" another element, there are no intervening elements present. Those skilled in the art will also recognize that a structure or feature that is referred to as being "disposed adjacent" another feature may have portions that overlap or underlie the adjacent feature.

It will be understood that, although the terms first, second, third, etc. may be used herein to describe various elements, components, regions, layers and/or sections, these elements, components, regions, layers and/or sections should not be limited by these terms. These terms are only used to distinguish one element, component, region, layer or section from another element, component, region, layer or section. Thus, a first element, component, region, layer or section discussed below could be termed a second element, component, region, layer or section without departing from the teachings of the present invention.

Furthermore, as illustrated in the accompanying drawings, relative terms such as "lower" or "bottom" and "upper" or "top" may be used herein to describe one element's relationship to another element. It should be understood that relative terms are intended to encompass different orientations of the device in addition to the orientation depicted in the figures. For example, if the device in one of the figures is turned over, elements described as being on the "lower side" of other elements would then be oriented on "upper sides" of the other elements. Thus, the exemplary term "lower" may encompass both a lower orientation and an upper orientation, depending on the particular orientation of the figure. Similarly, if the device in one of the figures is turned over, elements described as "below" or "beneath" other elements would then be oriented "above" the other elements. Thus, the exemplary term "below" or "beneath" can encompass both an orientation of above and below.

It should be further understood that the terms "include" and/or "comprising," or "include" and/or "include," or "have" and/or "have," or "carry" and/or "carry," or "contain" and/or "contain," or "involve" and/or "involve" are open, i.e., meaning including but not limited to. The terms "comprises," "comprising," "including," and/or "having," when used in this disclosure, specify the presence of stated features, regions, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, regions, integers, steps, operations, elements, components, and/or groups thereof.

Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.

As used in this disclosure, "about" or "substantially" shall generally mean within 20%, preferably within 10%, and more preferably within 5% of a given value or range. The numerical values set forth herein are approximate, meaning that the terms "about," "about," or "substantially" may be inferred if not explicitly stated.

As used in this disclosure, the phrase "at least one of A, B and C" should be construed to mean logic (a or B or C) that uses a non-exclusive logical or. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.

The term "mechanical-acoustic" as used in this disclosure refers to any sound, vibration or movement of a user that can be detected by an accelerometer or gyroscope. Thus, the accelerometer is preferably a high frequency tri-axial accelerometer capable of detecting a wide range of mechanical-acoustic signals. Examples include respiration, swallowing, organ (lung, heart) movement, movement (scratching, exercise and/or movement), speaking, bowel movement, coughing, sneezing, and the like.

As used in this disclosure, the term "two-way wireless communication system" refers to an on-board component of a sensor, wireless controller, and other electronic components that provides the ability to receive and transmit signals using at least one of Near Field Communication (NFC), wi-Fi/internet, bluetooth Low Energy (BLE), and cellular communication protocols for wireless communication. In this way, output may be provided to an external device, including a cloud-based device, a personal portable device, or a caregiver's computer system. Similarly, commands may be sent to the sensor, such as by an external controller, which may or may not correspond to an external device. Machine learning algorithms can be employed to improve signal analysis and, in turn, delivery to a physicianCommand signals of the therapy sensor (including the stimulator of the medical sensor) for providing haptic signals useful in therapy to a user of the medical device. More generally, these systems may be incorporated into a processor, such as a microprocessor of an electronic device located onboard or physically remote from the medical sensor. An example of a wireless controller is a Near Field Communication (NFC) chip, including an NFC chip. NFC is a radio technology that enables two-way short-range wireless communication between devices. Another example of a wireless controller is A chip or BLE system on a chip (SoC) that enables devices to communicate through standard radio frequencies rather than through cables, wires or direct user behavior.

As used in this disclosure, the term "flexible" or "bendability" refers to the ability of a material, structure, device, or device component to deform into a curved or bent shape without undergoing a transition that introduces significant strain (e.g., strain that characterizes the point of failure of the material, structure, device, or device component). In an exemplary embodiment, the flexible material, structure, device, or device component may be deformed into a curved shape without introducing greater than or equal to 5% strain at the strain sensitive region, greater than or equal to 1% for some applications, and greater than or equal to 0.5% for yet other applications. As used herein, some, but not all, of the flexible structures are also stretchable. Various properties provide the flexible structures (e.g., device components) of the present invention, including material properties such as low modulus, bending stiffness, and flexural stiffness; physical dimensions, such as a 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 membrane and opening or mesh geometries.

As used in this disclosure, the term "stretchable" refers to the ability of a material, structure, device, or device component to withstand a strain without breaking. In an exemplary embodiment, the stretchable material, structure, device, or device component may withstand greater than 0.5% strain without breaking, greater than 1% strain without breaking for some applications, and greater than 3% strain without breaking for yet other applications. As used herein, many stretchable structures are also flexible. Some stretchable structures (e.g., device components) are engineered to be capable of undergoing compression, elongation, and/or torsion, thereby being capable of deforming without breaking. The stretchable structure comprises a film structure comprising a stretchable material, such as an elastomer; a flexure structure capable of stretching, compressing and/or twisting motion; and structures having island bridge geometries. The stretchable device assembly includes a structure having stretchable interconnects, such as stretchable electrical interconnects. As used herein, for embodiments in which the device is mounted directly to the skin, the device may be characterized as stretchable, including stretchable and flexible, to achieve good conformal contact with the underlying skin when desired. By "conformable" is meant a device, material or substrate having a sufficiently low bending stiffness and a sufficiently high elasticity to allow the device, material or substrate to adopt a desired profile, including a profile that may vary over time, such as a profile that allows for conformal contact with a surface having a pattern of relief or recessed features, or. In certain embodiments, the desired contour is a contour of tissue in a biological environment, such as skin or epidermis layers.

As used in this disclosure, the term "elastomer" refers to a polymeric material that can be stretched or deformed and restored to its original shape without substantial permanent deformation. The elastomer typically undergoes substantially elastic deformation. Useful elastomers include elastomers comprising polymers, copolymers, composites, or mixtures of polymers and copolymers. An elastomeric layer refers to a layer comprising at least one elastomer. The elastomeric layer may also contain dopants and other non-elastomeric materials. Useful elastomers include, but are not limited to, thermoplastic elastomers, styrenic materials, olefinic materials, polyolefins, polyurethane thermoplastic elastomers, polyamides, elastomers, PDMS, polybutadiene, polyisobutylene, poly (styrene-butadiene-styrene), polyurethane, polychloroprene, and silicone. In some embodiments, the elastomeric stamp comprises an elastomer. Exemplary elastomers include, but are not limited to, silicon-containing polymers such as polysiloxanes, including poly (dimethylsiloxane) (i.e., PDMS and h-PDMS), poly (methylsiloxane), partially alkylated poly (methylsiloxane), poly (alkylmethylsiloxane), and poly (phenylmethylsiloxane), silicon modified elastomers, thermoplastic elastomers, styrenic materials, olefinic materials, polyolefins, polyurethane thermoplastic elastomers, polyamides, synthetic rubbers, polyisobutylenes, poly (styrene-butadiene-styrene), polyurethanes, polychloroprenes, and silicones. In one embodiment, the flexible polymer is a flexible elastomer.

As used in this disclosure, the term "encapsulation" or "encapsulation" refers to the orientation of a 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 a structure such that it is surrounded by one or more other structural portions. "fully encapsulated" refers to the orientation of a structure such that it is fully surrounded by one or more other structures. The present invention encompasses devices having partially or fully encapsulated electronic devices, device components, and/or inorganic semiconductor components.

Embodiments of the present invention are described in detail below with reference to the accompanying drawings. The following description is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses. The broad teachings of the disclosure can be implemented in a variety of forms. Therefore, while this invention includes particular examples, the true scope of the invention should not be so limited since other modifications will become apparent upon a study of the drawings, the disclosure, and the appended claims. For purposes of clarity, the same reference numbers will be used in the drawings to identify similar elements. It should be understood that one or more steps within a method may be performed in a different order (or simultaneously) without altering the principles of the present invention.

Soft, flexible and wearable sensors provide the ability to continuously collect physiological parameters related to human health. These systems provide the ability to provide continuous biofeedback and even therapeutic benefits through on-board analysis and edge calculations in the future, as has been previously disclosed. A key limitation of almost all existing wearable sensors is the motion artifact related signal quality degradation. This involves a lack of accurate sensing of heart rate, respiration rate, body position, swallowing, speaking, crying, or other respiratory signals in a scene where a living subject is moving. Furthermore, core body temperature sensing remains a challenge for non-invasive skin-mounted sensors. Thus, the ability to provide continuous measurement of skin-mounted core body temperature would provide significant clinical utility.

It is an object of the present invention to provide a new wearable sensor that achieves significantly improved motion and ambient temperature resistance sensing through new device mechanics and design and algorithms for noise subtraction. This new wearable device utilizes differential measurement of the output from the sensors, where one sensor measures physiological signals from the body and related environmental and overall body movement signals and the other sensor measures at least related environmental and overall body movement signals, allowing for efficient cancellation of noise, such as related environmental and overall body movement signals, during rest and movement.

In certain aspects, the invention relates to an electronic device for measuring a physiological parameter of a living subject. As shown in fig. 1C and 2B, in one embodiment, the electronic device comprises: at least a first Inertial Measurement Unit (IMU) and a second IMU, the first IMU and the second IMU being time synchronized with each other 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 data streams from the first IMU and the second IMU.

In certain embodiments, the first IMU is configured to measure data comprising a first signal and a second signal related to a physiological signal of the living subject, and the second IMU is configured to measure data comprising at least the second signal. The signal strength of the first signal measured by the first IMU is greater than the signal strength of the second signal measured by the first IMU.

In certain embodiments, the data measured by the first IMU and the second IMU are processed such that subtracting the second signal measured by the second sensor from the second signal measured by the first sensor produces a stronger first signal that is the signal of interest.

In certain embodiments, the second signal is related to at least one of an environment, motion, and vibration.

In certain embodiments, the data measured by the second IMU includes the first signal and the second signal.

In certain embodiments, a signal-to-noise ratio (SNR) of signals measured by the first IMU and the second IMU together is lower than a first SNR of signals measured by the first IMU alone or a second SNR of signals measured by the second IMU alone.

In certain embodiments, both the first IMU and the second IMU are in operable mechanical communication with the skin of the living subject.

In certain embodiments, one of the first IMU and the second IMU is in operative direct mechanical communication with the skin of the living subject for sensing physiological signals of the body, and the other of the first IMU and the second IMU is in operative indirect mechanical communication with the skin of the living subject.

In certain embodiments, the first IMU and the second IMU are in operative direct mechanical communication with the skin of the living subject.

In certain embodiments, one of the first IMU and the second IMU is separate from the remaining rigid components of the electronic device.

In certain embodiments, the electronic device further comprises at least a first thermal sensing unit and a second thermal sensing unit, wherein one of the first thermal sensing unit and the second thermal sensing unit is thermally isolated from the ambient environment and configured to measure the body temperature of the living subject, and the other of the first thermal sensing unit and the second thermal sensing unit is configured to measure the ambient temperature.

In certain embodiments, each of the first and second thermal sensing units is embedded in a respective one of the first and second IMUs.

In certain embodiments, the electronic device is configured to measure a range of physiological information from the activity of the cardiopulmonary system and movement of the nuclei to a collection of various processes related to respiration, speech, swallowing, wheezing, coughing and sneezing across the chest, esophagus, pharynx and mouth.

In certain embodiments, the electronic device is configured to separate signals associated with the cardiopulmonary system and related processes from signals generated due to body movement.

In certain embodiments, the electronic device is configured to spatiotemporal map movement of skin at this region of the anatomy to which the electronic device is attached during cardiac and respiratory activity.

In certain embodiments, the electronic device is configured to continuously measure temperature, heart Rate (HR), respiration Rate (RR), activity level, and body orientation across a range of strenuous activities and conditions.

In certain embodiments, the electronic device is configured to monitor key symptoms of a patient suffering from a covd-19 infection to track progression of recovery and response to therapy in a hospital and/or home.

In certain embodiments, the electronic device is configured to measure any respiratory or exercise-related digital biomarker associated with a cough, a swallow, and/or a particular exercise-related activity.

In certain embodiments, the electronic device is configured to evaluate cough when the living subject is moving or not moving, and/or measure muscle movement when the living subject is moving.

In some embodiments, the electronic device further comprises a two-way wireless communication system electronically coupled to the electronic device and configured to transmit an output signal from the electronic device to an external device.

In certain embodiments, the external device is a mobile device, a computer, or a cloud service.

In certain embodiments, the two-way wireless communication system is further configured to deliver commands from the external device to the electronic device.

In certain embodiments, the two-way wireless communication system includes a controller that communicates wirelessly using at least one of Near Field Communication (NFC), wi-Fi/internet, bluetooth Low Energy (BLE), and cellular communication protocols.

In some embodiments, the electronic device further includes a custom application having a user interface disposed in the external device to allow a user to configure and operate the electronic device for data collection, data transmission, data storage and analysis, wireless charging, and user condition monitoring.

In some embodiments, the custom application is configured to allow simultaneous time-synchronized operation of a plurality of the electronic devices.

In certain embodiments, the electronic device further comprises a power module coupled to the first IMU, the second IMU, and the MCU for providing power to the first IMU, the second IMU, and the MCU.

In certain embodiments, the power module includes at least one battery for providing the power. In certain embodiments, the battery is a rechargeable battery.

In certain embodiments, the power module further comprises a wireless charging module for wirelessly charging the rechargeable battery.

In certain embodiments, the power module further includes a fault protection element that includes a short circuit protection component or circuit to avoid battery faults.

In certain embodiments, the second IMU is placed in a manner that the second IMU is bent and folded over the battery.

In certain embodiments, the electronic device further comprises a flexible printed circuit board (fPCB) having flexible and stretchable interconnects electrically connected to electronic components including the first IMU, the second IMU, and the MCU, and the power module.

In certain embodiments, the electronic device further comprises an elastomeric encapsulant layer at least partially surrounding the electronic component and the flexible and stretchable interconnect to form a tissue-facing surface and an environment-facing surface attached to the living subject, wherein the tissue-facing surface is configured to conform to a skin surface of the living subject.

In certain embodiments, the encapsulation layer is formed of a flame retardant material.

In certain embodiments, the elastomeric encapsulant layer is a waterproof and biocompatible silicone shell.

In certain embodiments, the electronic device further comprises a biocompatible hydrogel adhesive for attaching the electronic device on a respective region of the living subject, wherein the biocompatible hydrogel adhesive is adapted such that signals from the living subject are operably conducted to the first IMU and the second IMU.

In certain embodiments, the electronic device is flexible and conformable to skin with a particular geometric polarity for installation in an anatomical location of interest of the living subject.

In certain embodiments, the electronic device is wearable, tissue mountable, or in mechanical communication with the skin of the living subject or direct mechanical communication. As used herein, mechanical communication refers to the ability of the sensor to directly or indirectly interface with skin or other tissue in a conformal, flexible, and direct manner (e.g., without an air gap), which in some embodiments allows for deeper insight and better sensing with fewer motion artifacts than accelerometers tied to the body (wrist or chest).

In certain embodiments, the electronic device is torsionally, stretchable, and/or bendable.

Various embodiments of the present technology comprise a soft, conformable, stretchable class of devices specifically configured to make mechano-acoustic recordings from the skin, capable of being used on almost any part of the body in a form that maximizes detectable signals and allows multi-modal operation such as electrophysiological recordings and neurocognitive interactions.

Another aspect of the invention provides an electronic device comprising a sensor network comprising a plurality of sensor units operably deployed on the skin of the living subject, the plurality of sensor units being time synchronized with each other and spatially and mechanically separated from each other; and an MCU electronically coupled to the plurality of sensor units for processing the data streams from the plurality of sensor units.

In certain embodiments, the plurality of sensor units are configured to measure the same physiological parameter or different physiological parameters.

In certain embodiments, each sensor unit of the plurality of sensor units comprises at least a first sensor and a second sensor, the first sensor and the second sensor being time synchronized with each other and spatially and mechanically separated from each other.

In certain embodiments, for each sensor unit, the first sensor is configured to measure data comprising a first signal and a second signal related to a physiological signal of the living subject, and the second sensor is configured to measure data comprising at least the second signal. The signal strength of the first signal measured by the first sensor is greater than the signal strength of the second signal measured by the first sensor.

In certain embodiments, the data measured by the first sensor and the second sensor of the sensor unit is processed such that subtracting the second signal measured by the second sensor from the second signal measured by the first sensor produces a stronger first signal, the stronger first signal being a signal of interest.

In certain embodiments, each of the first sensor and the second sensor comprises the IMU.

In certain embodiments, the electronic device further comprises a plurality of thermal sensing units.

In certain embodiments, the MCU is operable to receive inputs from synchronized outputs of a plurality of thermal sensor units having at least one thermal sensing unit for an ambient environment and at least one thermal sensing unit in direct thermal communication with the body thermally isolated from the ambient environment with built-in sensor thermal isolation material.

In certain embodiments, the electronic device is configured to automatically switch modes of operation including at least a first mode when the living subject is at rest and a second mode when the living subject is in high motion.

Yet another aspect of the invention provides an electronic device 1001 for measuring a physiological parameter of a living subject 1000, as shown in fig. 34A-34C. The electronic device 1001 comprises a first sensor 1002 adapted to detect a first set of data related to the living subject 1000 and a second set of data different from the first set of data; and a second sensor 1004 for detecting a third set of data substantially similar to the second set of data. In operation, the first sensor 1002 and the second sensor 1004 are time synchronized to allow the third set of data from the second sensor to be used to substantially cancel the second set of data from the first sensor.

In certain embodiments, the first sensor and the second sensor are spatially and mechanically separated from each other.

In certain embodiments, the first sensor and the second sensor are spaced apart by more than zero and less than a predetermined distance.

In certain embodiments, each of the first sensor and the second sensor comprises an IMU, a thermal sensor, and/or a pressure sensor.

In certain embodiments, the first set of data is a physiological signal of the living subject and the second set of data is a signal related to the environment, motion, and/or vibration at the first sensor.

In certain embodiments, the third set of data is a signal related to the environment, motion, and/or vibration at the second sensor.

In certain embodiments, both the first sensor and the second sensor are in operable mechanical communication with the skin of the living subject.

In certain embodiments, the first sensor is operable to be in direct mechanical communication with the skin of the living subject for sensing physiological signals from the body, and the second sensor is operable to be in indirect mechanical communication with the skin of the living subject.

In certain embodiments, the first sensor and the second sensor are in operative direct mechanical communication with the skin of the living subject for sensing physiological signals from the body to assess pulse transit time.

A further aspect of the invention provides an electronic device 1001 for measuring a physiological parameter of a living subject 1000, as shown in fig. 34A-34C. The electronic device 1001 comprises a first sensor 1002 adapted to detect a first set of data related to the living subject 1000 and a second set of data different from the first set of data; and a second sensor 1004 for detecting a third set of data substantially similar to the second set 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 a region of the living subject 1000 where a physiological signal of the living subject 1000 is measurable; the second sensor 1004 is positioned such that there is a second distance d2 between the 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.

In one embodiment, the second sensor 1004 is positioned above the first sensor 1002 as shown in fig. 34A.

In one embodiment, the second sensor 1004 is positioned remotely from the first sensor 1002, as shown in fig. 34B-34C.

In certain embodiments, the measurement of the physiological parameter may result from mechanical-acoustic signals from the heart rate, respiration rate, body position, swallowing count, crying time, speaking time, singing, coughing, and differential movement of a particular body part (e.g., torso movement relative to the head, hand movement relative to the wrist, calf movement relative to the knee) when the sensor is mounted across anatomical boundaries, as well as other respiratory signals both at rest and during movement.

In certain embodiments, the derivation of these physiological resistances to exercise allows for applicability across a wide range of medical professions and acuity ranging from emergency care, general medical care, ambulatory medical care, rehabilitation, and consumer health, particularly in high exercise settings.

In certain embodiments, the nucleus senses resistance to environmental temperature fluctuations and clothing.

In certain embodiments, a single MCU receives inputs from the synchronized outputs of multiple IMU sensors, where the single IMU sensor is in differential mechanical communication with the body.

In certain embodiments, a single MCU receives inputs from synchronized outputs of a plurality of thermal sensors having at least one thermal sensor for an ambient environment and at least one thermal sensor in direct thermal communication with the body thermally isolated from the ambient environment with built-in sensor thermal isolation material.

In certain embodiments, the novel mechanics of the sensor/device allow for torsion, stretching, bending to achieve low profile designs and thermal/mechanical isolation of the individual sensing elements in the device.

In certain embodiments, the novel mechanics of the sensor/device enable the rigid components of the device to be physically separated from the sensing elements, thereby enabling installation in unique anatomical locations for high data fidelity. Additional advantages include the ability to blur the sensor from view to reduce the patient's shame. This represents an umbilical function to allow discrete sensing in sensitive locations, where the body of the sensor is mounted in a position that is more easily obscured by clothing.

In certain embodiments, the thermal isolation material and layer allow for improved thermal sensing of core body temperature against ambient temperature fluctuations.

In some embodiments, the sensor/device is configured such that the mode of operation in the case of high motion allows for automatic switching to the anti-motion output. For example, a preferred measurement of heart rate may be an ECG while stationary, however, the patient may be in a situation where they are actively moving. In this case, in case the ECG-based heart rate is unreliable, the sensor may start actively interrogating the dual IMU to obtain heart rate derivatives.

In certain embodiments, the sensor/device has the ability to switch or activate dual sensing functions in the event of high motion to improve accuracy but save power in the rest setting.

The techniques described herein may be implemented in dedicated hardware (e.g., circuitry), in programmable circuitry suitably programmed in software and/or firmware, or in combinations of dedicated and programmable circuitry. Thus, embodiments may include machine-readable media 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 disk 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 memories, or optical cards

Other types of media/machine-readable media suitable for storing electronic instructions.

These and other aspects of the invention are described further below. Without intending to limit the scope of the invention, exemplary instruments, devices, methods, and related results thereof according to embodiments of the invention are presented below. It should be noted that titles or subtitles may be used in the examples for the convenience of a reader, but the title or subtitle should in no way limit the scope of the invention. Furthermore, certain theories are presented and disclosed herein; however, whether these theories are correct or not, they should in no way limit the scope of the invention, so long as the invention is practiced in accordance with the invention without regard to any particular theory or scheme of action.

Examples

Differential cardiopulmonary monitoring system for artifact removal physiological tracking for athletes, workers and covd-19 patients

Soft skin integrated electronic sensors can provide continuous measurement of various physiological parameters with broad relevance to the future of human health care. However, motion artifacts can corrupt the recorded signals, particularly signals associated with mechanical features of the cardiopulmonary process. The design strategy presented herein addresses this limitation by differential operation of a matched pair of high bandwidth accelerometers located on portions of the anatomy that exhibit strong spatial gradients in motion characteristics. These dual sensing devices, when mounted at locations spanning the suprasternal notch and manubrium, allow for measurement of heart rate and sound, respiratory activity, body temperature, body orientation and activity level, along with swallowing, coughing, speaking and related processes, while being insensitive to environmental conditions during daily activities, strenuous exercise, high intensity physical labor, and even swimming. Deployment of patients with covd-19 allows clinical-grade flow monitoring of critical symptoms of disease, even during rehabilitation protocols.

In particular, exemplary work utilizes a pair of time-synchronized high bandwidth accelerometers (inertial measurement units (IMUs)) at opposite ends of a skin-interfacing device that positions one of the IMUs at an Suprasternal Notch (SN) and the other IMU at a manubrium (SM). Differences in the movement of the skin associated with cardiac and respiratory activity between these regions result in differences in the signals captured by these IMUs. In contrast, the overall movement of the neck and body core produces nearly identical responses. Thus, simple differential measurements cleanly eliminate common mode features, thereby separating signals associated with heart lung and related processes from signals generated due to body movement. An additional benefit of this architecture is that the temperature sensors integrated in these IMUs can be used in a similar differential manner to produce an estimate of core body temperature, which is largely immune to the environment. Here, careful selection of the thermal aspect of the device layout, rather than the inherent anatomical gradient, will produce the necessary differential response.

The following section presents (i) the design of an automated apparatus incorporating matched high bandwidth IMU pairs with optimized soft mechanics for high measurement sensitivity and accurate time synchronization across SN and SM; (ii) Results of the spatiotemporal mapping of the movement of skin at this region of the anatomy during cardiac and respiratory activity; (iii) Examples of modeling and design methods for utilizing these IMUs in dual temperature sensing of core body temperature with minimal impact of thermal environment; (iv) Demonstration of continuous differential measurements of temperature, HR and Respiration Rate (RR) with references to the most accurate commercial sensors across a series of strenuous activities and conditions; and (v) instructions for patient use in recovery from a covd-19 infection to track key symptoms of the disease, even during a high intensity physical rehabilitation protocol.

Results

Design and characterization

The platform utilizes a thin flexible printed circuit board (fPCB) in an open architecture with an elastomer encapsulated structure that completely seals the system to physically isolate the electronic device from the environment and facilitate sterilization for reuse. The layout produces soft mechanical properties for comfortable mounting on the skin, even at sensitive areas of the body. The contact-less docking interrogator supports wireless charging and initiates data download in an automated fashion to eliminate user burden.

Fig. 1A shows the device positioned across SN and SM mounted on the bottom of the neck. This unique anatomical location allows measurement of a rich range of biophysical information from the activity of the cardiopulmonary system and movement of the nucleosome to the various sets of processes related to respiration, speech, swallowing, wheezing, coughing and sneezing across the chest, esophagus, pharynx and mouth.

Fig. 1B presents an exploded schematic view of a soft shell and fPCB with passive/active chip scale components. Top and bottom encapsulating films (thickness, 0.3mm;Silbione RTV 4420) of silicone elastomer mechanically isolate the active portions of the system in a sealed enclosure that allows operation even when immersed in water or exposed to perspiration. The design also conforms to guidelines of the U.S. disease control and prevention center for cleaning and disinfection using 70% alcohol (fig. 7). fPCB utilizes a patterned copper (12 μm) -polyimide (PI; 25 μm) -copper (12 μm) laminate (DuPont, AP 7164R) to define conductive traces of 80 and 150 μm width. The layout (fig. 8) contains separate islands for the circuit components (body), each of the two IMUs (IMU 1 and IMU 2), and the wireless charging coil. Serpentine shaped traces interconnect the islands to mechanically decouple the IMUs from each other, which is necessary in accurate differential measurement of MA signals at the skin surface. In particular, two pairs of narrow filamentous serpentine structures electrically connect the IMU to the body in a manner that minimizes mechanical limitations. The fPCB also contains multiple zones to allow static bending during the assembly process of folding the system into a compact configuration. The image in fig. 1C shows the overall dimensions relative to a us coin (diameter, 24.26 mm). The encapsulated device was 46mm by 22mm in size; its thickness is less than 9mm and its weight is less than 6.35g.

Experimental studies as shown in fig. 1D and Finite Element Analysis (FEA) calculations as shown in fig. 1E confirm that the strain in the copper of the serpentine interconnect remains below the fracture limit (c=1%) throughout the assembly process and during operation under different types of external loads: a 90 ° twist (left frame in fig. 1D-1E), a 45% stretch (middle frame in fig. 1D-1E), and a 180 ° bend (right frame in fig. 1D-1E) are deformed. This soft, stretchable design can accommodate skin deformation at the SN without fatigue or breakage and with minimal irritation and discomfort. The reinforcement layer below the critical area of the platform reduces the probability of damage to the weld caused by bending/stretching. The result is a mechanically robust platform that enables highly sensitive measurements of fine movements and whole body kinematics of the skin over a wide frequency range. A description of additional design features (including those associated with thermal measurements) follows.

The block diagram in fig. 1F outlines the overall system operation. Three main components include a device, a tablet with a custom application as a user interface, and a cloud platform for data storage and analysis. The device used a BLE SoC (bluetooth low energy system on chip) (nordic semiconductor (Nordic Semiconductor), nRF 52840), PMIC (power management integrated circuit) (texas instruments (Texas Instruments), BQ 25120), 4 gigabit NAND flash (micro), MT29F4G 01) and two identical IMUs, each with an embedded temperature sensing unit (semiconductor of law) (LSM 6 DSL). Wireless charging involves voltage and current protection as support for a 75 milliamp 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 social interactions and/or capturing MA signals at multiple body locations (fig. 9). Fig. 10 depicts a state diagram of the system to illustrate behavior before and after configuration, followed by deployment on a subject. The figure also shows the operation during data collection, charging and data transfer.

The full automation of critical operational steps minimizes user burden, which is particularly important for the use of patients with covd-19, as described later. The user need only install the device during use and place the device on the wireless charging platform when removed. When not on the charging platform, the sensor continuously stores data from both accelerometers onto the internal memory module; while on this platform, the device charges the data and at the same time streams the data to the user interface device via the bluetooth protocol. The user interface then transmits the data to the 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 complies with HIPAA (health insurance portability and accountability act (Health Insurance Portability and AccountabilityAct)), and the interface application uses HTTPS transport layer security (TLS 1.2), as well as algorithms for encryption/decryption of application programming interfaces and standards for in-storage encryption (AES-256).

Core body temperature estimation with dual temperature sensing

The simplest result of the dual sensing architecture is to measure the temperature of the skin (T _skin ) Is largely independent of the environment (T _amb ) Interference, following the schemes previously described in other contexts. Here, in the configuration shown in fig. 2A, the sensors embedded in IMU1 and IMU2 produce a temperature of 0.004 ℃ every 5 seconds (adjustable to a sampling rate of up to 52 Hz). The IMU1 sits directly adjacent the skin, separated only by a thin bottom encapsulation layer (0.3 mm thick silicone elastomer). A 6mm thick thermally insulating foam (polyurethane mixture) with a metal film (12 μm thick aluminized polyethylene) minimizes coupling to the environment by convection, conduction and radiation. The temperature at IMU1 is largely dependent on core body temperature, regulated by the effective thermal properties of the tissue and environmental conditions. The IMU2 resides on the outward facing side of the device with only a top encapsulation layer above in order to maximize coupling to the environment (fig. 2B-2C). The plurality of bottom layers comprising the adhesive film, bottom encapsulation, fPCB, battery and thermally insulating foam limit heat transfer from the skin to IMU 2.

Transient heat transfer analysis associated with three-dimensional (3D) heat conduction and natural convection quantifies these effects. The boundary conditions include a constant temperature (T _core ) And convective coupling (T _amb ). Parameters include room temperature, T _amb =18 ℃ to 24 ℃, and convection coefficient h=5 to 30Wm ^-2 K ^-1 . FIG. 2D highlights T _core ＝37℃，T _amb =22 ℃ and h=10wm ^-2 K ^-1 Temperature distribution across the skin and the area surrounding the device. Cross section along ABThe temperature profile of the face shows the results inside the device, where T _core =37 ℃ (fig. 2E) and T _core =38 ℃ to 40 ℃ (fig. 11). FIG. 2F outlines T _core Two IMUs at 37 ℃ (T _diff ) Temperature difference between them. As can be expected, T is reduced _amb And/or increasing h increases T _diff . As a specific example, for h=5wm ^-2 K ^-1 When T _amb At =24℃, T _diff =1.72 ℃, and when T _amb ＝18℃，T _diff =2.52 ℃. In addition, for h=30W m ^-2 K ^-1 When T _amb At =24℃, T _diff =3.80 ℃, but when T _amb At 18 ℃, T _diff =5.55 ℃. In the same way, FIG. 12 shows at T _core Temperature differences with associated temperature profiles in fig. 13 under conditions of =38 ℃ to 40 ℃. Analysis also quantifies the effects of changes in ambient temperature, core body temperature, convection coefficients, and other critical parameters. The measurement of the temperature difference, along with the subject-specific thermal model, yields a robust estimate of core body temperature.

A simple demonstration involves a subject wearing the device in an environment with an ambient temperature of 18.2 ℃, then moving between areas with temperatures of 21.3 ℃ and 19.5 ℃ every 3 to 8 minutes, and finally remaining in place as the ambient temperature increases from 19.5 ℃ to 24.2 ℃ for 7 minutes. The temperature results recorded from IMU1 and IMU2 appear in the top graph in fig. 2G. The middle graph shows the temperature difference. The subject-specific model converts these temperature measurements into estimates of core body temperature (third line in fig. 2G), determined by equation (17) in the "1-D analytical model for thermal properties" section of the materials and methods, T _core ＝T _amb +(T _IMU1 -T _IMU2 ) = (B/a-D/C), where T _amb Is the ambient temperature, T, inferred from IMU2 _IMU1 Is the temperature deduced from IMU1, T _IMU2 Is the temperature inferred from IMU2, and (B/a-D/C) is the amount of heat transfer coefficient and thickness of the various material layers depending on the skin and device. Details of the structure, values, equations and modeling method appear in FIG. 14 (see 1-D score for thermal property section in materials and methods)And (5) analyzing the model). A thermocouple placed under the tongue (green curve in the bottom graph of fig. 2G) produces a reference value that approximates the core body temperature. Fig. 15 compares the results to core temperatures estimated from measurements at IMU1 and IMU 2. The difference remains less than about 0.5 ℃ across ambient temperatures of 19.5 ℃ to 24.2 ℃. Fig. 16 shows a branchman-oltmann diagram of data. Sensing using IMU1 (red) alone resulted in an average difference of 3.81 ℃ and SD of 0.22 ℃ compared to oral measurement; the dual sensing method (blue) yields an average difference of 0.01 ℃ and SD of 0.18 ℃. The 1D heat transfer model and the 3D FEA model of temperature dynamics for analysis (fig. 17) can capture the fundamental aspects of these demonstrations. The analysis and 3D FEA results are very consistent across different ambient temperature scenarios with associated thermal convection coefficients and core temperatures (36.3 ℃) similar to the experimental results in fig. 2G and 17 (B).

Dual sensing from SN and SM

Dual temperature sensing is largely dependent on design choices that produce different levels of sensitivity to the body and ambient temperatures of IMU1 and IMU 2. For dual MA sensing, the differential response derives primarily from spatial gradients in motion across the mounting location, specifically from SN (location of IMU 1) and SM (location of IMU2 (2.5 cm below IMU 1)). The time-space diagram of the motion of this region of the anatomy, as determined by 3D particle tracking velocimetry (3D-PTV), provides quantitative insight into the differential motion associated with respiration and cardiac activity at these two locations and adjacent regions. The 3D-PTV relies on optical techniques to track the Lagrangian path (Lagrangian path) of fiducial markers on the skin in 3D using stereo imaging in a manner that reproduces the point measurement modality of the IMU. Here, the 3D-PTV can capture the dual-sensing nature of 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 motion across the neck, the region containing SN and SM (fig. 3A). The displacement and vector contour fields result from interpolation of fiducial points at each frame based on dironi triangulation (Delaunay triangulation) (fig. 3B). The results characterized herein correspond to a representative velocity of the subject measured near the peak of the cardiac cycle at rest, relative to the velocity between cycles. The results reveal a significant difference between the motions at SN and SM as the basis for differential detection.

Figures 3C-3E show additional details corresponding to the z-axis displacement curve through several cardiac cycles during breath-hold after a brief exercise (20 push-ups) period. The peak displacement at SN is-50% greater than the peak at SM, as shown in fig. 3C. In contrast, the displacement associated with body movement is nearly the same (as expected, but not explicitly shown here), thereby allowing for efficient subtraction. The differential result appears in fig. 3D. Fig. 3E shows a color contour plot of z-axis displacement at the peak of the cardiac cycle highlighted by the arrows in fig. 3D and 18 (C-D).

Similar considerations apply to the differential dynamics associated with breathing. Figures 3F-3H and 18 (E-F) outline the displacement distribution of three respiratory cycles while swinging slightly back and forth along the z-axis. Figure 3F shows the movements at SN and SM, where for each case the response contains contributions from body movements and respiration. The differential results shown in fig. 3G isolate the respiratory signal to a large extent, as shown in fig. 18 (E-F). It should be noted that the small period features in these data are derived from cardiac activity. The color contour plot of z-axis displacement at the peak of inspiration further highlights the spatial gradient that enables differential detection, as shown in fig. 3H. The data captured with the device shows similar trends (fig. 19) and a simple 1D analytical model (fig. 20; see section "analytical modeling of differential accelerometer" in materials and methods) can capture the fundamental aspects of these behaviors (fig. 21), which reveal a clear basis for differential detection at SN and SM.

Differential MA sensing minimizes motion artifacts in respiratory and cardiac monitoring

As verified by 3D-PTV, cardiac and respiratory activity produces motion with different amplitudes at SN and SM. Similar amplitudes are produced by movement of the nucleus. Thus, by eliminating large common mode features caused by body motion, simple subtraction of the MA signal measured at these two locations greatly improves the accuracy and reliability of the respiratory and cardiac activity measurements. Fig. 4A-4D outline the results captured using the device platform in combination with an IMU having high fidelity tri-axial acceleration measurement capability.

The flowchart in fig. 22 (a) shows a method for calculating RR (breath-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 maximum energy) in the spectrum of the range of interest across RR (6 to 60 RPM) within a 1 minute time window, as shown in fig. 22 (F). As shown in fig. 22 (B-C), the differential signal largely eliminates common mode "noise" associated with walking. Other signal components, such as signal components due to heart activity, are outside of the frequency range associated with respiration and/or have a power below a threshold, as shown in fig. 22 (D-E).

The flow chart in fig. 23 (a) highlights the corresponding algorithm of HR (heart Beat Per Minute (BPM)). As with RR, differential data removes features from walking, running, jumping, and related activities. Band pass filtering of the spectrum for the 1 minute time window with cut-off frequencies of 45 and 170BPM eliminates the low frequency signal from slow body processes and the high frequency content from vocalization and related events as shown in fig. 23 (B-C). This frequency envelope captures the fundamental features associated with the SI peaks associated with heart sounds, equivalent to the features observed in the Seismogram (SCG), as shown in fig. 23 (D). The frequency with the greatest energy and the frequency with at least 80% of the greatest energy (fig. 23 (E)) are used as the basis for the weighted average to determine HR.

Fig. 4B-4D highlight the results obtained during sitting, walking, running and jumping. The first case involves resting on a chair with controlled RRs of 6RPM, 10RPM, 12RPM, 15RPM, 20RPM, 30RPM, and 35RPM (0 to 7 minutes). The subject intentionally controls the expiration/inspiration (1:1 ratio) time with a timer while moving. Next, the subject walked (8 to 21 minutes, 90 steps/minute, average stride length of 50 cm), was running (22 to 29 minutes, 180 steps/minute, average stride length of 85 cm) and was jumping (31 to 36 minutes, jumping vertically every 2 to 3 seconds over a height of about 40 em), all of which were under similar controlled RR. Walking and running produces a repeating sequence of high amplitude pulse signals that dominate the data from IMU1 (red) and IMU2 (black). In contrast, the differential signal is characterized by a clear periodic response (15 BPM; blue) associated with respiration, as shown by the left box in FIG. 4B (purple dotted area in FIG. 4A) and the middle box in FIG. 4B (yellow shaded area in FIG. 4A). This differential signal also contains information about heart activity as significant S1 and S2 peaks of SCG (1.5 seconds; green dashed area in the middle box in fig. 4B).

FIGS. 4C-4D compare RR and HR results extracted based on the normal and differential methods. In the absence of body movement (e.g., sitting), the values are similar (blue shaded areas in fig. 4C-4D). During walking movements (walking, running and jumping), the results from the single accelerometer data (black dots) are highly variable compared to the results of the differential data (blue dots) for both HR and RR (yellow, red and green shaded areas in fig. 4C-4D). Differential sensing produces accurate results not only for walking and running, but also for jumping. FIG. 24 (A-C) shows a Broadelmann plot of RR (single sensor, FIG. 24 (A-B); dual sensor, FIG. 24 (C)). Results for IMU1 (red) and IMU2 (black) showed an average difference of-0.84 RPM (IMU 1) and-0.90 RPM (IMU 2), and SDs of 8.72RPM (IMU 1) and 10.49RPM (IMU 2). The difference results (blue) show an average difference of 0.27RPM and SD of 1.93 RPM. Likewise, for HR, the results of IMU1 (red) and IMU2 (black) showed average differences of-2.23 BPM (IMU 1) and-4.12 BPM (IMU 2) and SDs of 13.92BPM (IMU 1) and 13.18 (IMU 2), and the results with differential data (blue) showed average differences of 0.01BPM and SD of 2.71BPM (fig. 25). When comparing single-sense and dual-sense performance based on the SD of extracted RR and HR, the differential signal from dual-sense shows 77% and 79% improvement over RR and HR, respectively, from single-sense data.

Examples during strenuous activities in athletic activities

Sports games, fitness training, physical labor and related activities present a great challenge for accurate measurement of RR and HR due to the rapid, dynamic and highly variable large amplitude accelerations of the body. Dual sensor platforms provide powerful functions in these and other environments. Fig. 5 highlights examples of riding, basketball, and swimming. For exercise on stationary bicycles (24 minutes; FIGS. 5A-5B, and 26-27), both single-sense (IMU 1) and dual-sense (differential) data produced HR results that matched HR results obtained with a reference device (general electric company (General Electronics), dash 3000). Since the influence of motion artifacts is limited in this case, the RR values are also similar. In contrast, differential sensing uniquely provided reliable measurements of HR and RR for basketball (11 min) subjects, as might be expected on the basis of the controlled study described previously (fig. 5C-5D). Additional benefits (3.5 to 5 minutes in fig. 5D) occur during extreme movements. The watertight envelope and internal non-volatile memory allow for use in water sports, as shown during swimming (5 minutes in fig. 5E-5F). Differential measurement methods are particularly valuable when the motion artifact and the target signal are in similar frequency ranges. During swimming, RR computation using signals from a single IMU is dominated by responses associated with swimming gestures. Because of the large amplitude of these accelerations, which is inconsistent with breathing, the algorithm processes the results as outliers, as shown in fig. 5F. The differential signal from the dual sensor greatly minimizes the signal associated with swimming gestures, thereby yielding clear features associated with the exhalation and inhalation cycles, and enabling calculation of RR. The differential signal produces accurate respiratory activity, but the pattern of breathing and swimming occurs in the same frequency range, as shown in fig. 5G-5H. A similar demonstration highlighted in fig. 28 involves push-ups with controlled breathing out of phase with the push-up cycle, such that RR signals cannot be distinguished from periodic body movements using data from IMU1 or IMU 2. In contrast, the differential signal shows a clear signature associated with the exhalation/inhalation phase that is well matched to the periodicity and amplitude of the controlled breath.

Examples during strenuous activities in physical labor

Given the need to continuously monitor critical cardiopulmonary parameters in harsh environments, worker health represents another area of opportunity. The demanding profession involving construction, mining, fire and related fields of work may benefit from a non-invasive, high fidelity monitoring system to detect fatigue, heat exhaustion and performance in a seamless manner and compatible with high motion artifacts and extreme environmental conditions of temperature, sound, etc. Fig. 28 highlights an example of manual labor including hammering nails, carrying boxes, and shoveling. The data in fig. 29 (a) shows that the differential signal exhibits a clear characteristic of heart activity that would otherwise be masked by the strong pulse characteristics associated with hammering. Similarly, in these cases, the respiratory feature can be easily extracted even during large and irregular features of body movement, as shown in fig. 29 (B-C).

Clinical deployment for monitoring recovery of covd-19 patients

An area of urgent concern is digital monitoring of critical symptoms in patients with covd-19 to track progression of recovery and response to therapy in hospitals and homes. In addition to RR, HR, body movement, and body temperature, measurements can also capture the intensity and frequency of coughing, speaking, and laughing events. In general, these factors are important for symptomatic assessment of disease and for indirect assessment of aerosol production. The study reported here involved a patient positive for covd-19 (49 years; female; height, 170em; weight, 107kg; type 2 diabetes, obesity, hypertension and cerebrovascular accident in 2018) equipped with a dual sensing platform and instructions for recording over the course of 8 days, as shown in figures 6A-6D. The patient captured 171 hours of data over 5 days of this time frame, including periods of dry cough and shortness of breath with oxygen therapy occurring during the recovery phase. Fig. 6A-6B show the cumulative cough count along with the downward trend of RR during this same interval, with blue dots representing 5 minute averages and orange lines showing the data after smoothing with Savitzky-Golay. Fig. 6C outlines HR, where the black and red lines show similar average and smoothed results, respectively. Fig. 6D presents the activity level (green bars) calculated by integrating the spectral power across the frequency range of 1 to 10Hz and the estimated core body temperature (red lines). The daytime corresponds to a time interval between 6 am and 6 pm, while the other times are considered to be nights. The average body temperature recorded relative to the first day (37.5 ℃) showed a drop of 0.5 ℃ compared to the eighth recovery day (37.0 ℃). This recovery period involves a strict and intensive physical rehabilitation regimen. The ability to track vital signs and key symptoms throughout can provide actionable clinical information about recovery and patient readiness to return to home. The widespread deployment of this technology can improve patient care, help manage epidemic situations, and also enhance disease awareness.

Materials and methods

Encapsulating an electronic device in a soft shell

The top and bottom molded layers of low modulus elastomer (Silbie 4420; each 300 μm thick) form a flexible package structure for electronic devices. The manufacturing process involves placing the electronic device on a bottom layer and then casting a uniform outer coating of liquid prepolymer onto the silicone elastomer (Ecoflex 0030). A top molding layer with spacers was installed on each short side of the mold and the assembly clamped together, closing the system to thermally cure in an oven at 70 ℃ for 20 minutes. Cooling to room temperature, removing the device, and eliminating excess elastomer from the periphery using a die cutter.

Forming a thermally insulating foam

A three-axis milling machine (Roland MDX 540) produces an aluminum mold with a concave shape. After coating the mold surface with a Release agent (Ease Release 200, smooth-On, USA) in the U.S., a liquid precursor of polyurethane foam (mixing ratio of A to B of 2:3; flexFoam-iT | III, smooth-On in the U.S.) was cast On the mold, and then after curing On a hot plate at 100℃for 30 minutes, a flat aluminum plate was pressed On the top side to produce an insulating foam. The use of a 5 μm thick double-sided tape (No. 5600, nitto Denko co., japan) to attach the reflective film (insulation blanket; swiss security product company (Swiss Safe Products)) to the flat bottom surface of the foam layer further improved the insulation properties. The final step of the process involves the use of CO ₂ A laser (general laser systems company (Universal Laser System inc.)) cuts the perimeter of the material into a final geometry.

Modeling of mechanical properties

The commercial software ABAQUS (ABAQUS analysis user manual 2010,6.10 version) defines the strain epsilon in the metal layer of the system. Simulation allows design parameters to be selected to ensure that the strain in copper (Cu) remains below the fracture limit (e=1%) to avoid mechanical failure during device assembly and during different types of deformations (tensile, bending and torsional). Thin Cu and PI films were modeled by a composite shell element (S4R). The number of elements in the model is about 2x10 ⁵ And the minimum element size is 1/8 of the narrowest interconnect width (100 μm). For all cases, the simulated grid convergence is guaranteed. Modulus of elasticity (E) and Poisson's ratio (v) for copper EC _U =119 GPa and v _Cu =0.34, and E for PI _PI =2.5 GPa and V _PI ＝0.34。

3D FEA modeling of thermal properties

Transient heat transfer analysis determines the effect of heat conduction and natural convection on the response of the temperature sensor. Tissue and internal sensor assemblies were modeled by hexahedral elements (DC 3D 8). The encapsulation layer was modeled using tetrahedral elements (DC 3D 4). The number of elements in the model is about 6x10 ⁵ And for all cases, the simulated grid convergence is ensured. The boundary conditions include a constant temperature (T _core ) And convection conditions (T) with ambient air at the free surface _amb ). The following parameters were used in the calculation: room temperature T _amb =18 ℃ to 24 ℃; convection coefficient h= (5 to 30) W m ^-2 K ^-1 The method comprises the steps of carrying out a first treatment on the surface of the For tissue, the thermal conductivity, heat capacity and mass density were 0.3W m ^-1 K ^-1 、1460Jkg ^-1 K ^-1 And 960kg mw ³ The method comprises the steps of carrying out a first treatment on the surface of the For thermoplastic chips, the thermal conductivity, thermal capacity and mass density were 0.21W m ^-1 K ^-1 、1090Jkg ^-1 K ^-1 And 1420kg m ^-3 The method comprises the steps of carrying out a first treatment on the surface of the For FR4, the thermal conductivity, thermal capacity and mass density were 0.343Wm ^-1 K ^-1 、1150J kg ^-1 K ^-1 And 1850kgm ^-3 The method comprises the steps of carrying out a first treatment on the surface of the For polyurethane foam, the thermal conductivity, heat capacity and mass density were 0.03W m ^-1 K ^-1 、1200J kg ^-1 K ^-1 And 85kgm ^-3 The method comprises the steps of carrying out a first treatment on the surface of the For PI, the thermal conductivity, thermal capacity and mass density were 0.21W m ^-1 K ^-1 、2100J kg ^-1 K ^-1 And 909kg m ^-3 The method comprises the steps of carrying out a first treatment on the surface of the For Ecoflex 00-30, the thermal conductivity, thermal capacity and mass density are 0.2w m ^-1 K ^-1 、1460J kg ^-1 K ^-1 And 1070kg m ^-3 The method comprises the steps of carrying out a first treatment on the surface of the And for Silbie 4420, the thermal conductivity, thermal capacity and mass density were 0.15W m ^-1 K ^-1 、1460J kg ^-1 K ^-1 And 970kg m ^-3 。

Measurement of displacement distribution by 3D-PTV

The experiment involved recordings from four synchronous high speed area scan cameras (2048 x1088 resolution; HT-2000M, emergent Co., ltd. (Emergent)) with 35mm imaging lenses (F1.4 manual focus; xinand Co., ltd.)) at a frame rate of 200 fps. The process focuses on tracking 300 fiducial points marked in a grid pattern across the neck covering SN, SM and adjacent areas. The study volume was 10cm by 8cm by 10cm illuminated by six arrays of 600 lumen LED light bars. Preprocessing, calibration, 3D reconstruction, tracking and post-processing use custom 3D-PTV codes. The image sequence is preprocessed by subtracting background noise and enhancing contrast. 3D calibration utilizes motion restoration structure techniques from multiple perspectives. After removing the effects of lens distortion, the inherent parameters of the individual cameras are estimated using a checkerboard calibration method. External parameters for all four cameras, including 3D translation and rotation matrices, are obtained by using a set of sparse points that match across view angles. Once all camera parameters are estimated, a dense set of fiducial points across multiple views can be detected at the sub-pixel level and reconstructed in 3D coordinates. The fiducial points of the 3D reconstruction are tracked using a hungarian algorithm (Hungarian algorithm) and linked by performing five frame gap closures to produce a long trajectory. The displacement, velocity and lagrangian acceleration are filtered and calculated using fourth order B-splines. The 3D displacement and vector contour fields are obtained by interpolating discrete fiducial points at each frame based on dironi triangulation. An Euler video magnification method (the Eulerian video magnification method) is used to magnify the image sequence during heart activity.

Program for dual sensing temperature and motion measurement

A double sided medical silicone adhesive (3M company (3M), 2477P) secures the sensor to the neck region (IMU 1 aligned over SN and IMU2 aligned over SM). The authors confirmed that all subjects in the study provided written informed consent, consent to published study images with blurred faces. All data in this study were captured using an IMU with a sensitivity of 0.061mg (gravitational acceleration), a sampling rate of 1666Hz (adjustable to at most 6664 Hz) and an acceleration measurement range of + -2 g (adjustable to at most + -16 g).

Protocols for human subject studies

These studies were approved by the northwest university institutional review board (theNorthwestern University Institutional Review Board, chicago, IL, USA) (STU 00202449 and STU 00212522) in Chicago, IL and registered on clinical trims gov (NCT 02865070 and NCT 04393558). All study-related procedures were performed according to the criteria listed in the declaration of helsinki (the Declaration of Helsinki) in 1964. For a covd-19 positive patient, a double-sided medical silicone adhesive (3M company, 2477P) secures the sensor to the neck region (IMU 1 aligned on SN and IMU2 aligned on SM) for more than 12 hours. For multiple day use, a medical grade transparent film (tergaderm, 3M company) was applied between the skin and the double-sided adhesive to eliminate irritation created by the adhesive. Clinical staff helps the patient place the sensors. After each data measurement session, the device was sterilized with 70% isopropyl alcohol and dried at room temperature, and the sterilization process was repeated twice.

Classifying signal features by machine learning to extract cough events

Fig. 31 shows a flow chart of the algorithm. The training data includes time-series z-axis acceleration data having characteristics associated with tapping, coughing, laughing, and oropharynx. Training of this classification algorithm uses 10 datasets from each class (subjects SP1 to SP 4), as shown in fig. 32. Feature extraction uses peak detection, spectral information, and spectrograms. The first step uses an adaptive threshold to identify events associated with a tap, cough, laugh, and clear throat from input signal levels evaluated across a sliding window having a width of 0.5 seconds. Each extracted event is then aligned with the center of the corresponding time frame to maximize the energy of the signal for post-processing based on a continuous wavelet transform (first row of fig. 31 (C)). The resulting images within the 0.12 second window form the basis for further analysis and classification (third row of fig. 31 (C)). Specifically, these extracted features are classified into four activities using a binary tree architecture of a Support Vector Machine (SVM), as shown in fig. 31 (D). First, in the SVM1 classifier, the hypopharynx activity is removed by the negative value of the SVM1 hyperplane. Next, the tap activity is classified from the SVM2 with a specific decision boundary (SVM 2 result value: 2.5). Finally, the SVM3 compares the category (cough and laugh) with another decision as shown in FIG. 31 (E)

The boundaries are separated.

1-D analysis model of thermal characteristics

The skin/tissue thickness and thermal properties of the two sensors are assumed to be approximately the same because the horizontal distance between them is relatively small. In this way, the core body temperature is determined from the temperature variation in the thickness direction of the device. The derivation is based on the device material layer (thickness t _i And thermal conductivity k _i As listed in fig. 14) to determine the IMU sensor and ambient temperature T _amb Temperature difference between to estimate core body temperature T _core . Steady state heat transfer equations for determining the temperature T in IMU1 and IMU2 are based on the equationsWhere z represents the coordinate in the thickness direction in fig. 14 (a).

Boundary conditions based on thermal conduction through the device layers of the IMU1 sensor may be expressed as

Based on these boundary conditions, the temperature of the IMU1 sensor may be expressed as

Similarly, boundary conditions based on conduction through device layer IMU2 sensors

Can be expressed as

T _t | _z＝0 ＝T _core (9)

FIG. 17 (A) shows that the temperatures of IMU1 and IMU2 between the 1-D analytical model and the 3-D FEA results are very consistent over the range of the relevant thermal convection coefficients h. A simplified 1-D model can be used to model the model by subtracting T _IMU1 -T _IMU2 To determine T _core The expression of (2) is as follows

Wherein the ratios (B/A) and (D/C) are given below and depend on h.

If the device is attached to different body locations (e.g., neck, head, arms, etc.) with different thermal properties, the skin/tissue thermal properties (i.e., thickness and thermal conductivity) of each location must be adjusted accordingly (depending on the anatomy of the skin/tissue layer). Experiments showed that the amount of change in the temperature of the battery during operation of the device was negligible (< 0.06 ℃) (fig. 33).

Analytical modeling of differential acceleration measurements

While most of the capability in differential acceleration measurements stems from the inherent differences in motion at SN and SM, additional contributions may stem from details associated with the device layout. A schematic of an analytical model capturing these structural differences is shown in fig. 20. All components are considered herein as rigid bodies and consider only 1-D movements along the z-axis. The harmonic displacement u applied to the rigid platform can be written as

u＝A ₀ sin(w ₀ t)(20)，

So that both sensors are accelerated along the z-axis. The IMU1 at SN is tied to a rigid platform that is considered the chest wall (i.e., the same displacement as equation (20)). IMU2 at SM is connected to this rigid platform by a spring with a stiffness k, where the magnitude k= Σ _i E _i S _i /L _i Where Ei, si and Li are respectively the young's modulus (young's modulus), effective area and height of the damper with damping ratio and the different materials/electronic layers between the platform and IMU2 (e.g. battery, device and silicone gel). Both IMU1 and IMU2 have a mass m. The damper in IMU2 provides a damping force proportional to the relative speed f= -cv, where the damping ratio Depending on the damping coefficient c and critical damping

Coefficient c _c . In the dual sensor, the silicone gel acts as a damper with a damping ratio ζ=1% to 12%. Since IMU1 is tied to a rigid platform, its acceleration is also the same as:

the acceleration of IMU1 is described by the Ordinary Differential Equation (ODE) kinematics equation

Equation (22) has a solution of the form

x＝Ae ^-wξt sin(w _d t+θ)+bA ₀ sin(w ₀ t-ε) (23)

Wherein the method comprises the steps of

The first term in equation (23) is a general solution and the second term is a solution. Using the initial condition x (t=0) =0, constants a and θ can be determined. For body movement, w ₀ ＝1～3(2π)s ^-1 ＝6～20s ^-1 . For "spring material" (battery, gel, device) in the sensor, the total height is about 8mm and the mass of the accelerator is about 0.07g, so w should be greater than-1000 s ^-1 . In this case, the solutions for A and θ are uncorrelated, since the first term will decay very rapidly in a few seconds, thereby reducing equation (23) to

x＝bA ₀ sin(w ₀ t-ε) (24)

Thus, the acceleration of the IMU2 may be determined as

Then the difference (IMU 1-IMU 2) is

Wherein the method comprises the steps ofAnd->/>

Because w > w ₀ ，Epsilon→0 and there is no phase difference between IMU1 and IMU 2. At the same time, b.fwdarw.1, so b ₀ 0 and the amplitude difference between IMU1 and IMU2 is very small.

Discussion of the invention

This illustrative example presents, among other things, a low-profile, lightweight, flexible wireless sensor that is tightly coupled to skin as a dual measurement interface for SN and SM in a modality for differential sensing of temperature and MA characteristics of a body process. These results allow measurement of a wide range of physiological parameters and activity behaviors, which overcomes the fundamental challenges in almost every existing monitoring system: motion artifacts. A comparison with previous studies on mechano-acoustic sensing methods is presented in table 1. Specific examples reported herein include tracking cardiac activity, respiratory sounds, body temperature and general activity across a range of controlled settings and natural activities in sports, physical labor and clinical medicine. There are many other implications for these techniques and basic ideas. Examples include rehabilitation of patients suffering from aphasia and/or dysphagia, where measurements of vocal activity and swallowing are possible during daily life, outside of a hospital or rehabilitation clinic, which is especially relevant for stroke survivors and patients suffering from chronic obstructive pulmonary disease. The ability to track these processes without the privacy problems associated with microphone recordings and in a manner that is not affected by ambient sound represents a key feature of the method. The availability of multi-axis information, including tri-axis acceleration measurements, tri-axis gyroscope data, and tri-axis magnetometer information, provides additional opportunities for these same platforms. Examples include quantitative measurements of cervical motion (fig. 30) of patients recovered from cervical surgery by using accurate vector data between IMU1 and IMU2, and whole body motion detection followed by whole body motion reconstruction to achieve rehabilitation or early atypical motion diagnosis for cerebral palsy.

Table 1: compared to previous studies on mechanical-acoustic sensing methods.

/>

The foregoing description of the exemplary embodiments of the invention has been presented only for the purpose of illustration and description and is not intended to be exhaustive or to limit the invention to the precise form disclosed. Many modifications and variations are possible in light of the above teaching.

The embodiments were chosen and described in order to explain the principles of the invention and its practical application to thereby enable others skilled in the art to utilize the invention and various embodiments and with various modifications as are suited to the particular use contemplated. Alternative embodiments will become apparent to those skilled in the art to which the present invention pertains without departing from its spirit and scope. The scope of the invention is, therefore, indicated by the appended claims rather than by the foregoing description and exemplary embodiments described therein.

Some references, which may contain patents, patent applications, and various publications, are cited and discussed in the present specification. Citation and/or discussion of such references is provided to clarify the description of the present invention only and is not an admission that any such reference is "prior art" to the present invention as described herein. All references cited and discussed in this specification are incorporated by reference in their entirety and to the same extent as if each reference were individually incorporated by reference.

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Claims

1. An electronic device for measuring a physiological parameter of a living subject, the electronic device comprising:

at least a first Inertial Measurement Unit (IMU) and a second IMU, the first IMU and the second IMU being time synchronized with each other 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 data streams from the first IMU and the second IMU.

2. The electronic device of claim 1, wherein the first IMU is configured to measure data comprising a first signal and a second signal related to a physiological signal of the living subject, and the second IMU is configured to measure data comprising at least the second signal, wherein a signal strength of the first signal measured by the first IMU is greater than a signal strength of the second signal measured by the first IMU.

3. The electronic device of claim 2, wherein the data measured by the first IMU and the second IMU are processed such that subtracting the second signal measured by a second sensor from the second signal measured by a first sensor produces a stronger first signal, the stronger first signal being a signal of interest.

4. The electronic device of claim 2, wherein the second signal is related to at least one of an environment, motion, and vibration.

5. The electronic device of claim 2, wherein the data measured by the second IMU includes the first signal and the second signal.

6. The electronic device of claim 2, wherein 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 alone or a second SNR of a signal measured by the second IMU alone.

7. The electronic device of claim 2, wherein both the first IMU and the second IMU are in operable mechanical communication with the skin of the living subject.

8. The electronic device of claim 7, wherein one of the first IMU and the second IMU is in operative direct mechanical communication with the skin of the living subject and the other of the first IMU and the second IMU is in operative indirect mechanical communication with the skin of the living subject.

9. The electronic device of claim 8, wherein the first IMU and the second IMU are in operable direct mechanical communication with the skin of the living subject.

10. The electronic device of claim 2, wherein one of the first IMU and the second IMU is separate from the remaining rigid components of the electronic device.

11. The electronic device of claim 1, further comprising at least a first thermal sensing unit and a second thermal sensing unit, wherein one of the first thermal sensing unit and the second thermal sensing unit is thermally isolated from an ambient environment and configured to measure a body temperature of the living subject, and the other of the first thermal sensing unit and the second thermal sensing unit is configured to measure an ambient temperature.

12. The electronic device of claim 11, wherein each of the first and second thermal sensing units is embedded in a respective one of the first and second IMUs.

13. The electronic device of claim 1, configured to measure a range of physiological information from the activity of the cardiopulmonary system and movement of the nucleosome to a collection of various processes related to respiration, speech, swallowing, wheezing, coughing and sneezing across the chest, esophagus, pharynx and mouth.

14. The electronic device of claim 13, configured to separate signals associated with the cardiopulmonary system and related processes from signals generated due to body movement.

15. The electronic device of claim 13, configured to spatiotemporal map movement of skin at this region of an anatomical structure to which the electronic device is attached during cardiac and respiratory activity.

16. The electronic device of claim 13, configured to continuously measure temperature, heart Rate (HR), respiration Rate (RR), activity level, and body orientation across a range of strenuous activities and conditions.

17. The electronic device of claim 13, configured to monitor key symptoms of a patient suffering from a covd-19 infection to track progression of recovery and response to therapy in a hospital and/or home.

18. The electronic device of claim 13, configured to measure any respiratory or exercise-related digital biomarkers associated with coughing, swallowing, and/or specific exercise-related activities.

19. The electronic device of claim 18, configured to evaluate coughing when the living subject is moving or not moving, and/or to measure muscle movement when the living subject is moving.

20. The electronic device of claim 1, further comprising a two-way wireless communication system electronically coupled to the electronic device and configured to transmit an output signal from the electronic device to an external device.

21. The electronic device of claim 20, wherein the two-way wireless communication system is further configured to deliver commands from the external device to the electronic device.

22. The electronic device of claim 20, wherein the two-way wireless communication system comprises a controller that communicates wirelessly using at least one of Near Field Communication (NFC), wi-Fi/internet, bluetooth Low Energy (BLE), and cellular communication protocols.

23. The electronic device of claim 20, further comprising a custom application having a user interface deployed in the external device to allow a user to configure and operate the electronic device for data collection, data transmission, data storage and analysis, wireless charging, and user condition monitoring.

24. The electronic device of claim 23, wherein the customization application is configured to allow simultaneous time-synchronized operation of a plurality of the electronic devices.

25. The electronic device of claim 20, wherein the external device is a mobile device, a computer, or a cloud service.

26. The electronic device of claim 1, further comprising a power module coupled to the first IMU, the second IMU, and the MCU for providing power to the first IMU, the second IMU, and the MCU.

27. The electronic device of claim 26, wherein the power module further comprises a fault protection element including a short circuit protection component or circuit to avoid battery faults.

28. The electronic device of claim 26, wherein the power module comprises at least one battery for providing the power.

29. The electronic device of claim 28, wherein the battery is a rechargeable battery.

30. The electronic device of claim 29, wherein the power module further comprises a wireless charging module for wirelessly charging the rechargeable battery.

31. The electronic device of claim 28, wherein the second IMU is positioned in a manner that the second IMU is bent and folded over the battery.

32. The electronic device of claim 26, further comprising a flexible printed circuit board (fPCB) having flexible and stretchable interconnects electrically connected to electronic components including the first IMU, the second IMU, and the MCU, and the power module.

33. The electronic device of claim 32, further comprising an elastomeric encapsulant layer at least partially surrounding the electronic component and the flexible and stretchable interconnect to form a tissue-facing surface and an environment-facing surface operably attached to the living subject, wherein the tissue-facing surface is configured to conform to a skin surface of the living subject.

34. The electronic device of claim 33, wherein the encapsulation layer is formed of a flame retardant material.

35. The electronic device of claim 34, wherein the elastomeric encapsulant layer is a waterproof and biocompatible silicone housing.

36. The electronic device of claim 1, further comprising a biocompatible hydrogel adhesive for attaching the electronic device on a respective area of the living subject, wherein the biocompatible hydrogel adhesive is adapted to enable signals from the living subject to be operably conducted to the first IMU and the second IMU.

37. The electronic device of any one of claims 1-36, being flexible and conformable to skin with a particular geometric polarity for installation in an anatomical location of interest of the living subject.

38. The electronic device of any one of claims 1-37, being wearable, torsionally stretchable and/or bendable.

39. An electronic device for measuring a physiological parameter of a living subject, the electronic device comprising:

a sensor network comprising a plurality of sensor units operably deployed on the skin of the living subject, the plurality of sensor units being time synchronized with each other and spatially and mechanically separated from each other; and

A microcontroller unit (MCU) electronically coupled to the plurality of sensor units for processing data streams from the plurality of sensor units.

40. The electronic device of claim 39, wherein the plurality of sensor units are configured to measure the same physiological parameter or different physiological parameters.

41. The electronic device of claim 39 or 40, wherein each sensor unit of the plurality of sensor units comprises at least a first sensor and a second sensor, the first sensor and the second sensor being time-synchronized with each other and spatially and mechanically separated from each other.

42. The electronic device of claim 41, wherein for each sensor unit, the first sensor is configured to measure data comprising a first signal and a second signal related to a physiological signal of the living subject, and the second sensor is configured to measure data comprising at least the second signal, wherein a signal strength of the first signal measured by the first sensor is greater than a signal strength of the second signal measured by the first sensor.

43. The electronic device of claim 42, wherein the data measured by the first sensor and the second sensor of the sensor unit is processed such that subtracting the second signal measured by the second sensor from the second signal measured by the first sensor produces a stronger first signal that is a signal of interest.

44. The electronic device of claim 42, wherein the second signal is related to at least one of an environment, motion, and vibration.

45. The electronic device of claim 41, wherein each of the first sensor and the second sensor comprises an Inertial Measurement Unit (IMU), a thermal sensor, a pressure sensor, and/or an optical sensor.

46. The electronic device of claim 45, wherein each of the first sensor and the second sensor comprises the IMU.

47. The electronic device of claim 46, further comprising a plurality of thermal sensing units.

48. The electronic device of claim 47, wherein each thermal sensing unit is embedded in a respective IMU.

49. The electronic device of claim 47, wherein the MCU is operable to receive synchronized outputs of the plurality of thermal sensor units having at least one thermal sensing unit for an ambient environment and at least one thermal sensing unit in direct thermal communication with the body thermally isolated from the ambient environment with built-in sensor thermal isolation material.

50. The electronic device of any of claims 39-49, configured to automatically switch modes of operation, wherein the modes of operation include at least a first mode when the living subject is at rest, and a second mode when the living subject is in high motion.

51. The electronic device of claim 39 configured to measure a range of physiological information from the activity of the cardiopulmonary system and movement of the nucleosome to a collection of various processes related to respiration, speech, swallowing, wheezing, coughing and sneezing across the chest, esophagus, pharynx and mouth.

52. The electronic device of claim 51 configured to separate signals associated with the cardiopulmonary system and related processes from signals generated due to body movement.

53. The electronic device of claim 51, configured to spatiotemporal map movement of skin at this region of an anatomical structure to which the electronic device is attached during cardiac and respiratory activity.

54. The electronic device of claim 51 configured to continuously measure temperature, heart Rate (HR), respiration Rate (RR), activity level, and body orientation across a range of strenuous activities and conditions.

55. The electronic device of claim 51, configured to monitor key symptoms of a patient suffering from a covd-19 infection to track progression of recovery and response to therapy in a hospital and/or home.

56. The electronic device of claim 51 configured to measure any respiratory or exercise-related digital biomarkers associated with coughing, swallowing, and/or specific exercise-related activities.

57. The electronic device of claim 56, configured to evaluate coughing when the living subject is moving or not moving, and/or to measure muscle movement when the living subject is moving.

58. The electronic device of claim 39, further comprising a two-way wireless communication system electronically coupled to the electronic device and configured to transmit an output signal from the electronic device to an external device.

59. The electronic device of claim 58, wherein the two-way wireless communication system is further configured to deliver commands from the external device to the electronic device.

60. The electronic device of claim 58, wherein the two-way wireless communication system comprises a controller that communicates wirelessly using at least one of Near Field Communication (NFC), wi-Fi/internet, bluetooth Low Energy (BLE), and cellular communication protocols.

61. The electronic device of claim 58, further comprising a custom application having a user interface deployed in the external device to allow a user to configure and operate the electronic device for data collection, data transmission, data storage and analysis, wireless charging, and user condition monitoring.

62. The electronic device of claim 61, wherein the custom application is configured to allow simultaneous time-synchronized operation of the sensor networks.

63. The electronic device of claim 58, wherein the external device is a mobile device, a computer, or a cloud service.

64. The electronic device of claim 39, further comprising a power module coupled to the sensor network for providing power to the sensor network.

65. The electronic device of claim 64, wherein the power module further comprises a fault protection element that includes a short circuit protection component or circuit to avoid battery faults.

66. The electronic device of claim 64, wherein the power module includes at least one battery for providing the power.

67. The electronic device of claim 66, wherein the at least one battery is a rechargeable battery.

68. The electronic device of claim 67, wherein the power module further comprises a wireless charging module for wirelessly charging the rechargeable battery.

69. An electronic device for measuring a physiological parameter of a living subject, the electronic device comprising:

a first sensor adapted to detect a first set of data related to the living subject and a second set of data different from the first set of data; and

a second sensor for detecting a third set of data substantially similar to the second set of data,

wherein in operation, the first sensor and the second sensor are time synchronized to allow the third set of data from the second sensor to be used to substantially cancel the second set of data from the first sensor.

70. The electronic device of claim 69, wherein the first sensor and the second sensor are spatially and mechanically separated from each other.

71. The electronic device of claim 71, wherein a separation of the first sensor and the second sensor is greater than zero and less than a predetermined distance.

72. The electronic device of claim 69, wherein each of the first sensor and the second sensor comprises an Inertial Measurement Unit (IMU), a thermal sensor, a pressure sensor, or an optical sensor.

73. The electronic device of claim 69, wherein the first set of data is a physiological signal of the living subject and the second set of data is a signal related to environment, motion, and/or vibration at the first sensor.

74. The electronic device of claim 73, wherein the third set of data is a signal related to an environment, motion, and/or vibration at the second sensor.

75. The electronic device of claim 69, wherein both the first sensor and the second sensor are in operable mechanical communication with the skin of the living subject.

76. The electronic device of claim 75, wherein the first sensor is in operative direct mechanical communication with the skin of the living subject for sensing physiological signals from the body, and the second sensor is in operative indirect mechanical communication with the skin of the living subject.

77. The electronic device of claim 76, wherein the first sensor and the second sensor are in operable direct mechanical communication with the skin of the living subject for sensing physiological signals from the body to assess pulse transit time.

78. The electronic device of any one of claims 69-77 that is flexible and conformable to skin with a particular geometric polarity for installation in an anatomical location of interest of the living subject.

79. An electronic device for measuring a physiological parameter of a living subject, the electronic device comprising:

wherein in operation, the first sensor is positioned such that there is a first distance d1 between the center of the first sensor and a region of the living subject where a physiological signal of the living subject can be measured;

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.

80. The electronic device of claim 79, wherein the second sensor is positioned above the first sensor.

81. The electronic device of claim 79, wherein the second sensor is located remotely from the first sensor.

82. The electronic device of claim 79, wherein each of the first sensor and the second sensor comprises an Inertial Measurement Unit (IMU), a thermal sensor, a pressure sensor, or an optical sensor.

83. The electronic device of claim 79, wherein the first set of data is a physiological signal of the living subject and the second set of data is a signal related to environment, motion, and/or vibration at the first sensor.

84. The electronic device of claim 83, wherein the third set of data is a signal related to an environment, motion, and/or vibration at the second sensor.

85. The electronic device of claim 79, wherein both the first sensor and the second sensor are in operable mechanical communication with the skin of the living subject.

86. The electronic device of claim 85, wherein the first sensor is operable to be in direct mechanical communication with the skin of the living subject for sensing physiological signals from the body, and the second sensor is operable to be in indirect mechanical communication with the skin of the living subject.

87. The electronic device of claim 85, wherein the first sensor and the second sensor are in operable direct mechanical communication with the skin of the living subject for sensing physiological signals from the body to assess pulse transit time.

88. The electronic device of any one of claims 79-87, being flexible and conformable to skin with a particular geometric polarity for installation in an anatomical location of interest of the living subject.