Wearable noninvasive cardiopulmonary monitoring technology

Abstract

Described herein is a wearable sensing system that acquires (S)eismocardiography, (P)hotoplethysmography, (I)mpedance pneumography and cardiography, surface (R)espiratory mechanomyography and electromyography, and (E)lectrocardiography signals for monitoring ventilation, cardiovascular dynamics, and respiratory muscle activity.

Description

Attorney Docket No. 10034-429W012025-226 WEARABLE NONINVASIVE CARDIOPULMONARY MONITORING TECHNOLOGYCross-Reference to Related Application

[0001] This application claims priority to and benefit of U. S. provisional patent application serial No. 63 / 778,275 filed March 26, 2025, which is fully incorporated by reference and made a part hereof.Summary

[0002] An exemplary system and method are disclosed that measures surface respiratory electrical myography (sEMG), surface respiratory mechanomyography (sRMG), seismocardiography (SCG), electrocardiography (ECG), bioimpedance, and photoplethysmography (PPG) using one or more electrodes (e.g., bioimpedance electrodes), an accelerometer circuit assembly, and a PPG circuit assembly and determine the a unified result of blood volume pulsation, respiratory volume surrogates, respiratory phase timings, and electrical and mechanical parasternal intercostal muscle activity in real time. The determined blood volume pulsation parameter, respiratory volume surrogate parameter, respiratory phase timing parameter, and electrical and mechanical parasternal intercostal muscle activity parameter can be outputted to a mobile device or a health portal to provide a prolonged vital sign monitor for the users.

[0003] The exemplary system is portable and self-containable as its circuit assemblies are configured to measure the sEMG, sRMG, SCG, ECG, bioimpedance, and PPG without any complex mechanisms or cable connections to external devices. Besides portability, the exemplary system is also power-efficient as it can measure the signals for a long period of time without battery replacement.

[0004] In an aspect, a system is disclosed comprising one or more electrodes configured to attach to a skin region (e.g., sternum) of a person, the one or more electrodes being configured to measure surface respiratory electrical myography (sEMG) and surface respiratory mechanomyography (sRMG) across the skin region; an accelerometer circuit assembly (i.e., accelerometer PCB) configured with accelerometers, the accelerometer circuit assembly being configured to measure seismocardiography (SCG) across the skin region, wherein the accelerometer circuit assembly is positioned on the skin region; a photoplethysmographic circuit assembly (e.g., optical PCB) configured with photodiodes, the photoplethysmographicAttorney Docket No. 10034-429W012025-226 circuit assembly being configured to measure photoplethysmography (PPG) across the skin region, wherein the a photoplethysmography circuit is positioned on the skin; a controller operatively coupled to the accelerometer circuit assembly and the photoplethysmographic circuit assembly, the controller including: a processor; and a memory having instructions stored thereon, wherein execution of the instructions causes the processor to receive, via the processor, measured sEMG, sRMG, SCG, and PPG signals; and determine, via the processor, blood volume pulsation parameter, respiratory volume surrogate parameter, respiratory phase timing parameter, and electrical and mechanical parasternal intercostal muscle activity parameter from the measured sEMG, sRMG, SCG, ECG, bioimpedance, and PPG signals, wherein the determined blood volume pulsation parameter, respiratory volume surrogate parameter, respiratory phase timing parameter, and electrical and mechanical parasternal intercostal muscle activity parameter are outputted to provide a prolonged heart monitor for the person.

[0005] In some embodiments, the controller is implemented in a mobile device including a network interface (e.g., Bluetooth) configured to communicatively operate with the one or more electrodes and the photoplethysmographic circuit assembly through a network.

[0006] In some embodiments, the determined blood volume pulsation parameter, respiratory volume surrogate parameter, respiratory phase timing parameter, and electrical and mechanical parasternal intercostal muscle activity parameter are subsequently employed for ambulatory care monitoring.

[0007] In some embodiments, the respiratory phase timing parameter is determined from an amplitude modulation operation of R-peaks in the measured ECG signal.

[0008] Additional advantages will be set forth in part in the description which follows or may be learned by practice. The advantages will be realized and attained by means of the elements and combinations particularly pointed out in the appended claims. It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive, as claimed.Brief Description of Drawings

[0009] The disclosed methods, apparatuses, and systems are explained in even greater detail in the following drawings. The drawings are merely exemplary, and certain features may be used singularly or in combination with other features. The drawings are not necessarily drawn to scale.Attorney Docket No. 10034-429W012025-226

[0010] Fig. 1A shows an exemplary wearable system comprising a main PCB board and two pairs of sensor boards placed at the second intercostal space.

[0011] Fig. IB shows an exploded view of an exemplary mechanical prototype.

[0012] Fig. 1C is a photo of an exemplary reSPIRE prototype on a participant.

[0013] Figs. ID and IE show exemplary time-series waveforms captured by the device from the cardiovascular (Fig. ID) and respiratory domains (Fig. IE).

[0014] Figs 2A-2D show an experiment protocol to evaluate an example of the fabricated system where Fig. 2 A shows reference spirometry and inspiratory mouth pressure (IPmo) were acquired, with breathing trainers used to introduce inspiratory and expiratory loads. Maximal inspiratory pressure (Plmax) and maximal expiratory pressure (PEmax) were obtained using standardized testing. Then, the protocol consisted of controlled breathing periods, incremental inspiratory and expiratory loaded breathing, and finally a period of stationary cycling with a 3-minute recovery period. Fig. 2B shows exemplary impedance pneumography (IP) waveform showing changes in respiratory rate and volume during controlled breathing. Fig. 2C shows IP and surface respiratory mechanomyography measured from the right parasternal intercostal space (sRMGR) during inspiratory and expiratory loading are depicted. Ten breaths were performed at each fixed threshold. Fig. 2D shows after a 1 -minute warmup, cycling was performed at a constant load for 5 -minutes and was followed by 3-minutes of recovery. Example timeseries from a single participant, normalized to the start of warm-up, are shown for ventilation, heart rate, pre-ejection period, and impedance cardiography C-amplitude (ICGC-amp).

[0015] Fig. 3 shows correlation analysis between fixed sample entropy (fSE) and reference pressure parameters. Lineplots show median+IQR for fSE parameters against (a) mean inspiratory mouth pressure (IPmo) and (b) expiratory load. Mean Spearman (p) coefficients are shown. fSE parameters were log-transformed before assessing their repeated measures correlation with (c) IPmo and (d) expiratory load. Note, some outlier points from a single subject (green) are not shown in (c) for better visualization. sRMG: surface respiratory mechanomyography, sEMG: surface respiratory electromyography, L: left, R: right.

[0016] Fig. 4 shows exemplary time and time-frequency representations of respiratory muscle activity, (a) Two example inspirations, with the respiratory phase denoted by the impedance pneumography (IP) signal, at 3 different inspiratory loads: 10% of maximal inspiratory pressure (Plmax), 30% of Plmax, and 50% of Plmax. Surface respiratoryAttorney Docket No. 10034-429W012025-226 mechanomyography (sRMG) and electromyography (sEMG) signals measured at the second intercostal space are shown in time and time-frequency representations, (b) Example expirations are shown at 10% of maximal expiratory pressure (PEmax), 30% of PEmax, and 50% of PEmax.

[0017] Fig. 5 shows boxplots demonstrating comparisons of phase-specific surface respiratory mechanomyography (sRMG) and electromyography (sEMG) band-powers, (a) Phase comparisons at each level of inspiratory loading, defined as % of maximal inspiratory- pressure (Plmax), (b) Phase comparisons at each level of expiratory loading, defined as % of maximal expiratory pressure (PEmax). **denotes pcO. OOOl.

[0018] Fig. 6 shows Tidal volume regression and cardiopulmonary dynamics during cycling, (a) Regression and Bland- Altman (BA) plot for impedance pneumography (IP)- based tidal volume (TV) estimation. Limits of agreement (95%) with repeated measures correction is detailed, (b) Meanpmstandard error of the mean (SEM) plots are shown, illustrating the cycling-induced changes in ventilation, heart rate (HR), pre-ejection period (PEP), and impedance cardiography C-amplitude (ICGC-amp). Time-series of ventilation were derived both from IP and reference spirometry, whereas time-series of PEP were extracted from ICG and seismocardiography (SCG) signals.

[0019] Fig. 7 is a block diagram of an example computing device upon which embodiments of the invention may be implemented;Detailed Description

[0020] Some references, which may include various patents, patent applications, and publications, are cited in a reference list and discussed in the disclosure provided herein. The citation and / or discussion of such references is provided merely to clarify the description of the disclosed technology and is not an admission that any such reference is “prior art’’ to any aspects of the disclosed technology described herein. In terms of notation, “|n]’’ corresponds to the nth reference in the list. For example, [1] refers to the first reference in the list. All references cited and discussed in this specification are incorporated herein by reference in their entirety and to the same extent as if each reference was individually incorporated by reference.

[0021] Example System

[0022] Fig. 1A shows the exemplary wearable system (i.e., reSPIRE™ system). As shown, the exemplary system is comprised of a multi-printed circuit board (PCB) sensingAttorney Docket No. 10034-429W012025-226 system 100 configured to be worn on the sternum with a plurality (e.g., five) Ag / AgCl gel electrodes. The system comprises a main board 102 and two pairs of sensor boards comprised of an accelerometer board 104 and an optical board 106 placed at the second intercostal space. Together, these boards facilitate the acquisition of nine modalities: single¬ lead ECG, four-electrode IP, a non-conventional ICG, altitude tracking, surface respiratory electrical myography (sEMG) of the parasternal second intercostal muscles, and bilateral measurements of SCG, multi -wavelength PPG, chest wall kinematics, and surface respiratory mechanomyography (sRMG) signals. The exemplary system monitors the sensors while storing data on a storage media such as a microSD card, with integrated hardware support for Bluetooth Low Energy (BLE) 5.2.

[0023] Fig. 1A shows a block diagram for the exemplary system 100. Figs. 1B-1E show the hardware casing, placement, and measured physiological waveforms of the exemplary system 100.

[0024] The requirements of the sensor placements can be fundamental to the design of the exemplary system 100, which is developed to harness the physiological signals available from sensors positioned on and around the sternum. The sternum can be an ideal measurement site for several reasons. First, the small-signal nature of SCG signals necessitates placing accelerometers close to the heart for adequate signal quality (Ashouri et al., 2018). Second, the sternum region also provides convenient access to the parasternal intercostal muscles, which are obligatory muscles of inspiration (De Troyer et al., 2005a). sEMG and sRMG signals measured at the parasternal second intercostal space show strong relationships with inspiratory muscle mechanical output (Lozano-García et al., 2021, 2022; Lin et al., 2019; Murphy et al., 2011). Expiratory muscle activity, considered as cross-talk during monitoring of inspiratory muscle activity, can be detected at this site, particularly from muscles such as the triangularis sterni (Hudson et al., 2024; De Troyer and Boriek, 2011). The electrical activity and mechanical contraction of these expiratory muscles, though less superficial than the parasternal intercostals, may couple into recorded waveforms due to the sensitivity of the sensing hardware. Finally, previous studies demonstrated that impedance pneumography (IP) signals can be measured from tetrapolar bioimpedance measurements around the sternum with short inter-electrode distances (Berkebile et al., 2021; Klum et al., 2018; Sanchez-Perez et al., 2022b).

[0025] To maximize sensitivity to the signals of interest, a bilateral placement centered on the sternum can be used with sensing arms located over the second intercostal spaces at the parasternal region, as shown in Figs. 1A-1C.Attorney Docket No. 10034-429W012025-226

[0026] Main printed circuit board. The main board 102, in some instances, comprises an area of 34.4 x 40.6 mm2, though other sizes are contemplated within the scope of this disclosure. The main board 102 serves as the functional and spatial core of the exemplary system 100. The main board houses, for example, a processing device 108 such as a Nordic nRF’5340 system on chip (SoC) (Nordic Semiconductor, Trondheim, Norway), which is built around a dual-core ARM Cortex-M33 as part of a u-blox NORA-B106 BLE module (u-blox, Thalwil, Switzerland). The SoC 108 interfaces with all peripherals, dictating the peripherals’ operational mode and transferring sensor data to a storage media (e.g., microSD card). The main board 102 further comprises a MAX77654 power management integrated circuit (PMIC) 110 (Analog Devices, Norwood, MA, USA) that combines battery charging and monitoring with programmable regulator outputs. The battery voltage is initially dropped down using an efficient single-inductor multiple-output (SIMO) regulator. To satisfy the low-noise requirements of sensitive sensor analog front-end (AFE)s, TPS7A720 (Texas Instruments, Dallas, Texas, USA) low-dropout (LDO) linear regulators can be used to establish the required supply voltages (e.g., 1.8 V and 3 V supply voltages).

[0027] Several sensing modalities can be employed by the main board 102 to simplify the external sensor boards 104, 106 for easier placement and greater modularity. The bioimpedance and biopotential AFEs are also included on the main board 102, given the main board’s central location in the exemplary system 100, which results in shorter and more symmetrical electrode paths to the bilateral pairs of electrodes. A MAX30009 bioimpedance AFE (Analog Devices, Norwood, MA, USA) 112 may be used for its reduced power consumption, integrated conformance to the IEC60601 standard on current excitation, and improved support for ICG applications. An ADS1291 biopotential AFE 114 (Texas Instruments, Dallas, Texas, USA) can be used to measure a single-lead ECG, as in previous studies’ systems (Chan et al., 2021), and sEMG from the same lead. The ADS1291 114 can support a 2 kHz sampling rate with a -3 dB bandwidth of 524 Hz, sufficient for sEMG purposes, and provides an optional right leg drive (RLD) amplifier. Finally, a BMP581 barometric pressure sensor 116 (Bosch, Gerlingen, Germany) can be included for added environmental context through altitude tracking.

[0028] The main board 102 comprises a plurality of connectors and interfaces, such as a USB-C port (USB 2.0) for charging and communication via USB-CDC protocols, a lithium polymer (LiPo) battery connector, and a serial wire debugger. Additionally, two latched flexible flat cable (FFC) connectors support the versatile placement of auxiliaryAttorney Docket No. 10034-429W012025-226 boards. The FFCs enable transmission of serial peripheral interface (SPI) communication and power lines for the sensor boards, with daisy-chained chip-select lines supporting up to two external sensors per FFC. Fig. IB is an exploded view showing components of the main board 102 of the exemplary system 100.

[0029] The first of two sensor boards in the exemplary system is the accelerometer board 104 (in some instances, the accelerometer board 104 has an area of approximately 14.7 x 20.0 mm2, though other sizes are contemplated within the scope of this disclosure). The accelerometer board 104 comprises one or more 3-D accelerometers 118, the ADXL355 (Analog Devices, Norwood, MA, USA), and FFC connectors to interface with both the main 102 and optical 106 boards. The ADXL355’s 118 low-noise floor of 25 pgN Hz can provide a measurement of SCG signals, which can present with amplitudes on the order of 10 mg (Inan et al., 2015). As the sRMG signals overlap in frequency content to cardiogenic vibrations, the selected accelerometer can capture vibrations and movements arising from both the heart and respiratory muscles, as well as the kinematics of the chest wall. The accelerometer board can comprise FFC connectors to receive the supply voltage (e.g., 1.8 V) and serial peripheral interface (SPI) lines from the main board, which can be passed along to the optical board. Fig. 1C shows the accelerometer board 104 of the exemplary system 100 when placed over the sternum.

[0030] An additional sensor board in the exemplary system 100 comprises the optical board 106. In some instances, the optical board 106 has an area of approximately 8.9 x 10.7 mm2, though other sizes are contemplated within the scope of this disclosure. The optical board comprises an AFE 120 and optical module 122 to capture multi-wavelength photoplethysmography (PPG) signals. The AFE 120 (e.g., aMAX86171 (Analog Devices, Norwood, MA, USA)) controls a light-emitting diode (LED) currents supplied to the emitters and acquires the light detected by the photodiode (I’D) of the optics module 122 (e.g., a SFH7072 (Osram, Munich, Germany) optical package). The optics module 122 comprises four SFH7072 emitters including two green (530 nm) LEDs, a red (655 nm) LED, and an infrared (IR) (940 nm) LED. Two optical return paths, PDs, are incorporated in the optics module 122, with a broadband (410-1100 nm) and IR-cut detector (402-694 nm). This AFE 120 and optics module 122 pairing supports control over individual LED currents, flexible sampling schemes, and low-noise optical acquisition with a small footprint. Each optical board 106 can be set up to sample green, red, and IR PPGs from both PDs, resulting in 6 waveforms per board. The AFE 120 can be interfaced to the main board 102 via an SPI bus, where the four communication lines are delivered alongside theAttorney Docket No. 10034-429W012025-226 requisite supply (1.8 V) and LED (3.5 V) voltages with an FFC connected to the accelerometer board 104. Fig. 1D shows the optical board 106 of the exemplary system 100 when placed over the sternum.

[0031] The firmware for the exemplary system 100 is developed using the Zephyr real¬ time operating system (RTOS) framework, which is an open-source project that provides multi-threaded operation, support for a range of SoCs, and built-in drivers for subsystems such as universal serial bus (USB) and file system management. Custom drivers interface with each sensor, providing sensor configurations and abstraction to multiple sensors. Interrupt triggers are set at FIFO thresholds, where available, to initiate a burst reading of data. To summarize the sensor configurations, ECG and sEMG waveforms are sampled at approximately 2 kHz, bioimpedance - both real and imaginary components - are sampled at approximately 250 Hz with an excitation current amplitude (peak) of 362 µA at 64 kHz, both accelerometers are sampled at approximately 500 Hz, both sets of multi -wavelength PPGs at approximately 64 Hz with LED currents of 16 mA, and barometric pressure at 2 Hz. LED currents are adaptively updated to keep the measurement values within an acceptable threshold (Ganti et al., 2021a). Timestamps for the data are generated from an internal timer sourced from a 32.768 kHz oscillator, with a resolution of approximately 122 µs. The device is set up to have two modes, controlled via a push-button on the top of the device, including a low-power idle state or an acti ve sampling mode where all sensors can be sampled at full sampling rates, and data is saved to the microSD card with a FatFs file system. These data files, containing raw binary sensor waveforms and associated metadata, can be extracted after measurements with a custom user interface written in Python. Data can be transferred from the exemplary system 100 via USB-CDC communication. Before starting data collection, a Unix timestamp can be sent to the exemplary system for synchronization.

[0032] Experimental Results and Additional Examples

[0033] A study was conducted to fabricate and evaluate the exemplary system and method.

[0034] Fig. 1B shows an exploded view of the exemplary fabricated system 100. The study adopted the fabricated system into a wearable form factor by designing and 3D printing a central enclosure for the mainboard and separate enclosures for the bilateral sensing arms, including the accelerometer and optical boards. The rigid housings were 3D printed using polylactic acid (PLA), whereas the flexible cable shields interconnecting the main and daughter boards were printed with a thermoplastic polyurethane (TPU) material.Attorney Docket No. 10034-429W012025-226 This flexibility facilitated the system to conform to the anatomy of the sternum for better adherence and raise compliance to withstand the wearer’s movements while protecting sensitive electrode wiring and FFCs. A larger FFC connector with stronger latching was used for the main board-accelerometer board connection for a more robust mechanical connection, whereas a smaller FFC was used for the accelerometer board-optical board connector, as they were housed in the same enclosure. The rigid housings were sealed with snap-fit lids and heat inserts, which secured the PCBs to the enclosures with screws. Two snap-fit electrode sockets were integrated into each sensing arm for 3M 2670 electrodes (3M, St. Paul, Minnesota, USA). An additional electrode socket was integrated into the main enclosure, connected to the RLD from the biopotential AFE, which can be disabled. The inter-electrode distance was optimized such that the voltage- sensing electrodes were approximately 8.8 cm apart, which, accounting for the sternum size, placed the electrodes 3 cm from either edge of the sternum. The accelerometers were affixed above the voltagesense electrodes. The placement of the current injection electrodes was configured to have a 2 mm gap (distally) from the voltage-sense electrodes. Additionally, a protrusion was incorporated into the arm enclosures for the optical interface, which interfaced with a 10x10x1 mm3optical window (Edmunds Optics, Barrington, New Jersey, USA) positioned between the skin and optics.

[0035] Experiment protocol. A protocol with a plurality of cardiovascular and respiratory stressors was designed to validate the fabricated system. Figs. 2A-2D show an experiment protocol to evaluate the fabricated system.

[0036] In Fig. 2A, the focus of the protocol was to assess the robustness of the respiratory modalities captured by the fabricated system. The cardiovascular sensing modalities were validated in previous studies, both in healthy volunteers and patient populations, employing wearable devices with similar hardware and sensor placement (Ganti et al., 2022; Chan et al., 2021; Berkebile et al., 2025; Shandhi et al., 2022). Therefore, the various respiratory sensing components of the fabricated system were evaluated to determine their utility for cardiopulmonary monitoring.

[0037] The study recruited 18 (6 females) young and healthy participants (age:26.2+4.0 yrs, height: 174.0+11.3 cm, weight: 72.1+22.1 kg, chest circumference: 93.5+13.7 cm; mean+SD) with no history of cardiopulmonary conditions. The fabricated system was first placed on the participant’s sternum at the 2ndintercostal level. Test data were taken over a 1 -minute period to verify the quality of the signals. Then, reference 3-lead ECG, and airflow signals were acquired using a Biopac MP160 data acquisition system with anAttorney Docket No. 10034-429W012025-226 ECG100C module and TSD117 spirometer (Biopac Systems, Goleta, CA, USA). Respiratory effort and continuous blood pressure signals were also obtained. An electronic inspiratory muscle monitoring device and trainer (KH2, POWERBreathe International Ltd., Southam, UK) was used to enforce inspiratory resistances and obtain reference inspiratory mouth pressure (PImo) values, which served as a measure of respiratory muscle mechanical output. Expiratory muscle trainers (EXI, POWERBreathe International Ltd., Southam, UK) were also used to introduce expiratory resistance. Disposable mouthpieces and filters were used for the spirometer and airway resistance devices.

[0038] Participants were seated upright throughout the protocol, with a nose clip to restrict nasal airflow when necessary. Initially, participants performed maximal inspiratory pressure (Plmax) maneuvers (Pessoa et al., 2014) with the KH2, starting near residual volume. To maximize volitional effort, this maneuver was repeated at least 5 times, up to 10 attempts total, with breaks between efforts. Once the three highest values varied less than 10%, a participant’s Plmax was designated as the mean of the three highest measurements. From tills Plmax value and population regression equations (Evans and Whitelaw, 2009), an estimate of each participant’s maximal expiratory pressure (PEmax) was obtained. This PEmax value was tested by introducing an equivalent expiratory load that the participants can attempt to overcome, starting near total lung capacity. The expiratory- load was decreased incrementally until the participant could overcome the resistance, or increased incrementally until the participant could no longer overcome the resistance, with breaks between each effort. The highest expiratory pressure that the participant exceeded served as an approximate PE max •

[0039] Next, in Fig. 2B, a spectrum of respiratory volumes and rates was enforced across 90-second periods of unloaded breathing with airflow captured by the spirometer. The periods included spontaneous breathing (i.e., no breathing instruction), slow breathing at shallow and deep volumes, and fast breathing at shallow and deep volumes. Breathing rate was dictated with visual and audio cues at 6 breaths per minute (brpm) and 15 brpm for the slow and fast breathing periods, respectively. Breathing depth during the shallow and deep periods was determined by the participant, with no target volume constraints.

[0040] In Fig. 2C, participants completed inspiratory threshold loading protocols (Lozano-Garcia et al., 2019). To evaluate the fabricated system’s ability to detect activity from expiratory muscles in the vicinity of the parasternal intercostais, incremental expiratory loading was applied using the same approach as inspiratory loading. Airwayresistance was applied during either inspiration or expiration using the KH2 and EXIAttorney Docket No. 10034-429W012025-226 devices, which were attached to the expiratory valve of the spirometer. Thresholds incrementally increased from 10% to 50% of the PImaxfor inspiratory loading or PEmaxfor expiratory loading, with increments of 10%. For each load, 10 breaths were performed. No instructions on breathing rate or duty cycle were given, so participants naturally adapted their breathing to surpass the pressure thresholds. Rest periods were granted between each threshold as needed. The ordering of inspiratory or expiratory loading was randomized to mitigate bias.

[0041] Lastly, participants underwent five minutes of seated constant-load cycling with a subsequent 3-minute recovery period while breathing through the reference spirometer. Cycling served as an acute cardiopulmonary stressor, eliciting increased heart rate (HR), cardiac output (CO), and ventilation (Burton et al., 2004) with corresponding shortening of systolic timing intervals (Pilz et al., 2023). Cycling resistance was set on a participantspecific basis to provide moderate intensity. After a 1 -minute cycling warm-up at 10 mph, a target speed of 15 mph was maintained for 5 minutes. HR was monitored to ensure it did not exceed 70% of the predicted max HR for submaximal exercise intensity (Tanaka et al., 2001). At the end of the recovery period, the reference sensors and fabricated system were removed, and data were extracted.

[0042] One participant was excluded from the analyses of the incremental loading portion of the protocol due to issues reaching maximal volitional effort during the Plmax test and their inability to overcome the added airway resistance at all levels. Additionally, one participant experienced an electrode issue affecting the sEMG signal during the incremental loading protocol and was excluded from sEMG analyses.

[0043] Python (v3.12.7) was used for processing and analysis of the data. The respiratory signals were first uniformly resampled to the highest sampling rate, 2 kHz, and filtered to the bands of interest. Specifically, bioimpedance was band-pass filtered [0.08-1 Hz] and smoothed with a second-order Savitzky-Golay filter and a 1 -second window. The left and right triaxial ACC waveforms were band-pass filtered [5-40 Hz] and combined into respective vector magnitudes to form the left (sRMGL) and right (sRMGR) signals. The sEMG signal was band-pass filtered [20-400 Hz], with additional notch filters at powerline frequency [60 Hz] and its harmonics.

[0044] The cardiovascular signals were resampled to 500 Hz before filtering.Bioimpedance was band -pass filtered [1-40 Hz] and differentiated using a third order Savitzky-Golay filter and 200 ms window. The dorso-ventral component of both ACC waveforms were band-pass filtered [1-40 Hz], though only the left (SCGL) signal was used.Attorney Docket No. 10034-429W012025-226 ECG was band-pass filtered [2-40 Hz]. Finally, PPG signals were band-pass filtered [1-8 Hz], The fabricated system’s signals and reference measurements were aligned by finding the lag between the IP volume surrogate signal and integrated spirometer waveform using cross correlation. This was verified against manual timestamps taken during each part of the protocol.

[0045] Discussion

[0046] The demand for cardiopulmonary monitoring is urgent and multifaceted. Each year, over 1.3 million Americans with heart failure (HF) are hospitalized (Martin et al., 2024), often with symptoms of impaired cardiopulmonary function that accompanies acute decompensation in HF (Schiff et al., 2003). Continuous monitoring can aid in detecting early signs of deterioration, guiding treatment options, and assessing patient readiness for discharge. Meanwhile, Duchenne Muscular Dystrophy (DMD) is a muscle- wasting disorder that leads to progressive respiratory muscle weakness and cardiomyopathy (Buddhe et al., 2018; Khirani et al., 2014), with patients surviving on average into their late twenties (Mercuri et al., 2019). Early detection of respiratory dysfunction and cardiomyopathy is crucial, as proactive management can extend survival and improve quality of life (Viollet et al., 2012; Ishikawa et al., 2011). Given the increased prevalence of sleep-disordered breathing in the general population (Peppard et al., 2013) and its early role in the progression to overt respiratory failure in DMD (Sawnani et al., 2015), sleep monitoring of cardiopulmonary function is a tool for detecting early dysfunction. Therefore, within the landscape of acute care and progressive disease management, there is a need for effective cardiopulmonary monitoring.

[0047] Wearable systems that noninvasively and continuously monitor salient indicators of cardiopulmonary health can address this need. However, given the complexity of the current state-of-the-art cardiopulmonary system, the physiological markers used to quantify its function are not standardized. The commonly monitored and researched physiological markers include heart rate (HR), HR variability (Kleiger et al., 2005), respiratory rate (RR) (Rolfe, 2019), and oxygen saturation, which can provide valuable insights into cardiovascular and respiratory function. However, these measures fail to encompass essential aspects of the cardiopulmonary system, such as cardiomechanical activity (Lewis et al., 1977), breathing pattern and depth (Hallett et al., 2025), and respiratory muscle activity (Doorduin et al., 2013). Integrating these physiological markers relevant to conditions like DMD and decompensated HF into a single wearable system can provide a method for assessing a patient’s cardiopulmonary health.Attorney Docket No. 10034-429W012025-226

[0048] Technological advancements have led to many non-invasive methodologies for collecting signals related to the physiology of interest. Current state-of-the-art cardiovascular sensing systems use electrocardiography (ECG) and photoplethysmography (PPG) signals to quantify the electrical activity of the heart and pulsatile blood flow, respectively (Serhani et al., 2020; Reisner et al., 2008). Low-noise accelerometry (ACC) signals obtained from the chest facilitate the detection of cardiogenic vibrations known as seismocardiography (SCG) signals (Inan et al., 2015), which can be used with the ECG to derive systolic timing intervals. ACC signals measured on the chest can also assess muscle fiber vibrations related to respiratory muscle activity transcutaneously, denoted as surface respiratory mechanomyography (sRMG) (Sarlabous et al., 2009). These vibrations, assessed at the parasternal and lower intercostal spaces, can show strong correlations to inspiratory muscle force (Lozano-Garcia et al., 2018). sRMG signals can be complemented by surface respiratory electrical myography (sEMG), measured across the same intercostal space for both mechanical and electrical assessment of respiratory muscle function (Ramsook et al., 2017; Lozano-Garcia et al., 2021). At the intersection of cardiovascular and respiratory monitoring is a sensing modality, thoracic bioimpedance. Using four electrodes placed across the thorax, the impedance cardiography (ICG), indicative of blood flow-induced conductivity changes (Ernst et al., 1999; Sherwood, Chair), and impedance pneumography (IP), indicative of lung volume and thoracic expansion (Seppa et al., 2010; Blanco-Almazan et al., 2019), waveforms can be continuously measured. While these modalities have been validated, there are few examples of integrated systems that attempt a comprehensive cardiopulmonary approach (Sanchez-Perez et al., 2022a; Klum et al., 2020; Lee et al., 2019; Frerichs et al., 2020). Previous studies have yet to produce a unified system that measures the breadth of cardiopulmonary parameters necessary to impact clinical practice for DMD and HF.

[0049] The instant study developed the exemplary system (i.e., reSPIRE™) configured to acquire (S)eismocardiography, (P)hotoplethysmography, (I)mpedance pneumography and cardiography, (R)espiratory surface electromyography and mechanomyography, and (Electrocardiography signals. From these signals, the exemplary system can simultaneously capture the electrical and mechanical activity of the heart, blood volume pulsation, respiratory volume surrogates, timing of respiratory phases, and indices of electrical and mechanical parasternal intercostal muscle activity. The study validated the exemplary system in a healthy population (n=18) with a protocol including controlled breathing periods, incremental inspiratory and expiratory loading based on maximalAttorney Docket No. 10034-429W012025-226 pressure tests, and stationary cycling with a recovery period. The study demonstrated the technological feasibility of the exemplary system for continuous monitoring across a range of cardiopulmonary challenges.

[0050] Example

[0051] Validation Study Protocol

[0052] A protocol with several cardiovascular and respiratory stressors was designed to validate the reSPIRE system. Fig. 2A provides a summary of the protocol’s activities. Given the relative immaturity of respiratory sensing methodologies compared to the more established cardiovascular sensing approaches, the primary focus of the protocol was to rigorously assess the robustness of the respiratory modalities captured by the reSPIRE system. The cardiovascular sensing modalities have been extensively validated in prior studies, both in healthy volunteers and patient populations, employing wearable devices with similar hardware and sensor placement (Ganti et al., 2022; Chan et al., 2021; Berkebile et al., 2025; Shandhi et al., 2022). The various respiratory sensing components of the reSPIRE system, therefore, were thoroughly evaluated to determine their utility for cardi opul monary monitoring.

[0053] This study was conducted with 18 (6 females) young and healthy participants (age: 26.2+4.0 yrs, height: 174.0+11.3 cm, weight: 72.1+22.1 kg, chest circumference: 93.5+13.7 cm; mean+SD) with no history of cardiopulmonary conditions. The reSPIRE system was first placed on the participant’s sternum at the 2nd intercostal level. Test data were taken over a 1 -minute period to verify the quality of the signals. Then, reference 3- lead ECG and airflow' signals were acquired using a Biopac MP160 data acquisition system with an ECG100C module and TSD117 spirometer (Biopac Systems, Goleta, CA, USA). Respiratory effort and continuous blood pressure signals were also obtained, but were not analyzed in this work. An electronic inspiratory muscle monitoring device and trainer (KH2, POWERBreathe International Ltd., Southam, UK) was used to enforce inspiratory resistances and obtain reference inspiratory mouth pressure (PInio) values, which served as a measure of respiratory muscle mechanical output. Expiratory muscle trainers (EXI, POWERBreathe International Ltd., Southam, UK) were also used to introduce expiratory resistances. Disposable mouthpieces and filters were used for the spirometer and airway resistance devices.

[0054] Participants were seated upright throughout the protocol, with a nose clip to restrict nasal airflow' when necessary. Initially, participants performed maximal inspiratory- pressure (Plmax) maneuvers (Pessoa et al., 2014) with the KH2, starting near residualAttorney Docket No. 10034-429W012025-226 volume. To maximize volitional effort, this maneuver was repeated at least 5 times, up to 10 attempts total, with breaks between efforts. Once the three highest values varied less than 10%, a participant’s Plmax was designated as the mean of the three highest measurements. From this Plmax value and population regression equations (Evans and Whitelaw, 2.009), an estimate of each participant’s maximal expiratory pressure (PEmax) was obtained. This PEmax value was tested by introducing an equivalent expiratory load that the participants would attempt to overcome, starting near total lung capacity. The expiratory load was decreased incrementally until the participant could overcome the resistance, or increased incrementally until the participant, could no longer overcome the resistance, with breaks between each effort. The highest expiratory pressure that the participant exceeded served as an approximate PEmax.

[0055] Next, a spectrum of respiratory volumes and rate was enforced across 90-second periods of unloaded breathing with airflow captured by the spirometer, as depicted in Fig.2B. The periods included spontaneous breathing (i.e., no breathing instruction), slow breathing at shallow and deep volumes, and fast breathing at shallow and deep volumes. Breathing rate was dictated with visual and audio cues at. 6 breaths per minute (brpm) and 15 brpm for the slow and fast breathing periods, respectively. Breathing depth during the shallow and deep periods was determined by the participant, with no target volume constraints. Participants then completed inspiratory threshold loading protocols (Lozano- Garcha et al., 2019), as shown in Fig. 2C. To evaluate the reSPIRE system’s ability to detect activity from expiratory muscles in the vicinity of the parasternal intercostals, incremental expiratory loading was applied using the same approach as inspiratory loading. Airway resistance was applied exclusively during either inspiration and expiration using the KI 12 and EXI devices, which were attached to the expiratory valve of the spirometer. Thresholds incrementally increased from 10% up to 50% of the Plmax for inspiratory loading or PEmax for expiratory loading, with increments of 10%. For each load, 10 breaths were performed. No instructions on breathing rate or duty cycle was given, so participants naturally adapted their breathing to surpass the pressure thresholds. Rest periods were granted between each threshold as needed. The ordering of inspiratory or expiratory loading was randomized to mitigate bias.

[0056] Lastly, participants underwent 5 minutes of seated constant-load cycling with a subsequent 3-minute recovery period while breathing through the reference spirometer. Cycling serves as an acute cardiopul monary stressor, eliciting increased HR, cardiac output (CO), and ventilation (Burton et al., 2004) with corresponding shortening of systolic timingAttorney Docket No. 10034-429W012025-226 intervals (Pilz et al., 2023). Cycling resistance was set on a participant-specific basis to ensure that the intensity was moderate. After a 1 -minute warm-up of cycling at 10 mph, a target speed of 15 mph was maintained for 5 minutes. HR was monitored to ensure that it did not exceed 70% of predicted max HR for submaximal exercise intensity (Tanaka et al., 2001).

[0057] At the end of the recovery period, the reference sensors and reSPIRE system were removed and data were extracted. One participant was excluded from the analyses of the incremental loading portion of the protocol due to issues reaching maximal volitional effort during the Plmax test and their inability to consistently overcome the added airway resistance at all levels. Additionally, one participant experienced an electrode issue affecting the sEMG signal during the incremental loading protocol, and was therefore excluded from sEMG analyses.

[0058] Preprocessing and Time Alignment

[0059] Python (v3.12.7) was used for processing and analysis of the data. The respiratory signals were first uniformly resampled to the highest sampling rate, 2 kHz, and filtered to the bands of interest. Specifically, bioimpedance was band-pass filtered [0.08-1 Hz] and smoothed with a second order Savitzky-Golay filter and a 1 -second window. The left and right triaxial ACC waveforms were band-pass filtered [5-40 Hz] and combined into respective vector magnitudes to form the left (sRMGL) and right (sRMGR) signals. The sEMG signal was band-pass filtered [20-400 Hz], with additional notch filters at powerline frequency [60 Hz] and its harmonics.

[0060] The cardiovascular signals were resampled to 500 Hz before filtering.Bioimpedance was band-pass filtered [1-40 Hz] and differentiated using a third order Savitzky-Golay filter and 200 ms window. The dorso- ventral component of both ACC waveforms were band-pass filtered [1-40 Hz], though only the left (SCGL) signal was used. ECG was band-pass filtered [2-40 Hz], Finally, PPG signals were band-pass filtered [1-8 Hz],

[0061] The reSPIRE system’s signals and reference measurements were aligned by finding the lag between the IP volume surrogate signal and integrated spirometer waveform using cross correlation. This was verified against manual timestamps, which were taken during each part of the protocol.

[0062] Signal Processing

[0063] Breath Extraction Respiratory signals were processed similarly to prior work (Berkebile et al., 2021; Sanchez- Perez et al., 2022a; Charlton et al., 2021). Onsets ofAttorney Docket No. 10034-429W012025-226 inspiration and expiration were detected from rolling 32-second windows with 80% overlap for the filtered IP signal and reference spirometry. Breath candidates were assessed for physiological plausibility (4-60 brpm) and signal quality against a template breath derived from each window using cross-correlation. Quality thresholds of 0.7 and 0.8 were used for IP and spirometer breath candidates, respectively. A final set of inhale -exhale indices for each breath candidate were then derived and used for respiratory parameter extraction.

[0064] Quantification of Respiratory Muscle Activity

[0065] The fixed sample entropy (fSE) of sRMGL, sRMGR, and sEMG signal amplitudes was used to quantify the degree of respiratory muscle activity while mitigating the influence of cardiac artifacts by penalizing regularity in the time-series (Lozano-García et al., 2018; Estrada et al., 2016). Time-series of fSE parameters were derived from moving windows of 500 ms with a step size of 50 ms. The tolerances were computed as 0.2 and 0.5 times the standard deviation of the sEMG and sRMG signals, respectively, during the entire incremental inspiratory loading period (Estrada et al., 2017). During breaths detected from the IP signal, the levels of inspiratory and expiratory muscle force and activation were computed as the mean of the fSE parameters for the duration of respective breathing phase.

[0066] From the 10 breaths performed at each inspiratory and expiratory load, fSE sRMGL and fSE sRMGR values of the 5 breaths closest to the median were automatically selected for analysis. This mitigated the risk of including low fidelity breaths by discarding values if the two sRMG were not in close agreement.

[0067] To better assess the extent of detectable expiratory muscle activity in the sEMG and sRMG signals, we generated time-frequency representations to visualize and quantify phase specific respiratory muscle activity. Spectrograms were created with a 400 ms Hanning window, 20 ms step size. Based on phase-specific activity observed during the incremental loading protocol, band-powers during inspiration and expiration were computed from the sEMG and sRMG signals using the phase context of the IP signal. This approach allowed us to further evaluate the level of airway resistance at which muscle activity in the loaded respiratory phase became distinguishable from the unloaded phase.

[0068] Estimation of Respiratory Volume and Timings

[0069] Respiratory features were derived directly from the IP and spirometer signals.The spirometer airflow waveform was first integrated into a volume signal. Then, peak-to- peak amplitude values were computed breath by breath from the spirometer and IP signals to quantify tidal volume (TV). For estimation purposes, only spirometer and IP breaths occurring within 250 ms of each other were utilized. IP amplitudes were converted into TVAttorney Docket No. 10034-429W012025-226 estimates via simple linear regression. Participant-specific models were used, as in prior work (Berkebile et al., 2021). For calibration, 3 breaths were selected randomly to fit the linear regression models, with one breath from each of the spontaneous, deep slow, and shallow slow breathing periods. After removing the breaths used for calibration, the remaining IP amplitudes were transformed into TV estimates and were evaluated against the reference spirometer values. Outlier reference TVs, defined as those that were 4 median absolute deviation (MAD)s away from the median, were removed. Multiple random seedings were tested to ensure that performance was stable across different calibrations.

[0070] Respiratory timings were computed in non-overlapping 15-second windows.Within each window, RR, inspiratory time (Tins), and expiratory time (Texp) were extracted from the inhale-exhale indices for each breath and were averaged across all breaths in the window. The errors between IP estimates and spirometer-derived reference timings were then evaluated.

[0071] Quantification of Cardiopulmonary Dynamics during Cycling Protocol

[0072] The cardiovascular processing in this work largely follows that of (Berkebile et al., 2025). We aimed to characterize hemodynamic responses during exercise and recovery using markers related to cardiac output and sympathetic activity. Following R-peak detection from the ECG signal, SCG and ICG signals were beat-segmented with a fixed 600 ms length, using zero-padding as needed. Similarity-based signal quality of SCG beats was determined using dynamic-time feature matching (DTFM) (Zia et al., 2020), and similarly ICG beats quality was assessed with dynamic-time warping (DTW) and cross¬ correlation against template beats. Beats were then ensemble-averaged in 15 -beat windows, step size of 1. After beat segmentation and quality indexing, HR was derived from the ECG, the C -point amplitude from the ICG (ICGC-amp), and pre-ejection period (PEP) from both the SCG and ICG signals.

[0073] Multiple candidate aortic opening (AO) points were derived from the prominent peaks of the SCG signal, with the median used to compute the PEP. In similar fashion, multiple conventional b-point candidates were computed from the ICG beats, including 1 % rise time, zero-crossing, isoelectric crossing, and maximum of first ICG derivative (D'Arbol et al., 2017). These methods were employed to mitigate transients that can occur when single-fiducial point approaches coincide with noisy beats. Time-series of the physiological features were computed as the mean of non-overlapping 5 -second windows throughout the cycling and recovery period. We also assessed the ventilatory dynamics from IP-derived respiratory features in comparison to spirometer-derived measures. ForAttorney Docket No. 10034-429W012025-226 both the IP and spirometer values, a time-series of ventilation was computed as the mean TV and RR in non-overlapping 15-second windows. The estimated TVs during cycling were derived from transformed IP amplitude values using the previously fitted regression models.

[0074] Finally, the dynamic cardiopulmonary responses across all participants were aggregated into time-series of mean+standard error of the mean (SEM) to illustrate overall trends and reliability against reference measures, where available.

[0075] Statistical Analysis

[0076] Statistical analyses were performed in R (v4.4.2) and Python (v3.12.7).Correlation analyses were performed to determine the relationships between the measured respiratory muscle activity and reference pressure measurements or resistive loads. Spearman’s rank correlation ( ) was used since relationships with sRMG and sEMG are not strictly linear. Correlations were computed for each participant between the PImo and fSE parameters, and during expiratory load and fSE parameters. Participant-specific coefficients were aggregated into a mean correlation with Fisher z-transformation. Additionally, repeated measures correlation (rrm), which estimates a shared regression slope across participants as an indicator of global association (Bakdash and Marusich, 2017), was applied to the reference measures and fSE parameters, the latter of which were log-transformed to improve linearity.

[0077] Phase-specific respiratory muscle activity across inspiratory and expiratory- loading was assessed with linear mixed effects models (Kuznetsova et al., 2017; Winter, 2013). To account for repeated measures, models included random slopes and intercepts for each participant. Band-powers derived from sRMG and sEMG spectrograms were compared pairwise across each level of inspiratory and expiratory loading using the emmeans package, with p-values adjusted using Tukey’s correction and Cohen’s d to estimate effect size.

[0078] The performance of IP-based estimation of TV was assessed using the coefficient of determination (R2) for each participant and Bland- Altman (BA) analysis with corresponding 95% limits of agreement (LoA) that were corrected for repeated measures (Caldwell, 2022; Bland and Altman, 2007). The mean absolute error (MAE) and mean absolute percent error (MAPE) metrics were computed for estimated TV as well as RR, Tins, and Texp.

[0079] reSPIRE System PerformanceAttorney Docket No. 10034-429W012025-226

[0080] The reSPIRE system specifications and performance are summarized in Table I, below.Parameter Value Active Current Consumption -7.69 rnA Idle Current Consumption -2.79 mA Battery Life (350 mAh battery) -45 hours System Weight 60 gShe 202.5 x 49.1 x 18.7 mm3Bioimpedance (IP and ICG)Sampling Rate 250 Hz Excitation Frequency 64 kHz Excitation Current Peak Amplitude 362 μA Biopotential (ECG and sEMG)Sampling Rate 2 kHz Bandwidth 524 Hz Accelerometry (SCG and sRMG)Sampling Rate 500 Hz Bandwidth 125 Hz Optical (PPG)Sampling Rate 64 HzLED current 1 mA Pressure ( Altitude)Sampling Rate 2 HzTable I

[0081] Of note, the current consumption while actively sampling all sensors was 7.69 mA on average, yielding a battery life of approximately 45 hours with the accompanying 350 mAh battery. Thus, the reSPIRE system exhibits a lower power profile compared to our group’s previous systems (Sanchez-Perez et al., 2022a; Ganti et al., 2021b), while simultaneously incorporating more sensors and collecting more data. This efficiency can be attributed to improved power management circuitry, usage of lower-power AFEs, and power-conscious firmware design. Additionally, no data packets were dropped across theAttorney Docket No. 10034-429W012025-226 entire validation protocol, underscoring the reliability of the RTOS-based sampling scheme. Finally, though the prototype enclosures were designed as a proof-of-concept, the entire system weighed only 60 g and can be readily adapted to suit other form factors or sensor placements.

[0082] The device can be further refined to incorporate more BLE functionality, such as guided placement, configuration of device, and real-time streaming of cardiopulmonary- markers. Additionally, the prototype PCBs and mechanical enclosures were not necessarily- optimized for size and could be miniaturized to better suit different patient populations, such as the pediatric population of patients with DMD. The use of flexible electronics and smaller electrode interfaces could lead to substantial reductions in device footprint, further improving the wearability and comfort of the device.

[0083] Correlations between respiratory mechanics and wearable-derived indices of respiratory muscle force

[0084] The Plmax values measures at the beginning of the protocol were 86.8+28.4 cmH₂O (mean±SD). This corresponded to inspiratory thresholds varying from 8.6±2.9 cmH₂O at 10% of PImax up to 43.4±14.5 cmH₂O at 50% of PImax. Across the same thresholds, mean PImo increased from 6.2±1.7 cmH₂O to 29.1±9.6 cmH₂O. The approximate PEmax values were 113.1±33.3 cmH₂O, with expiratory thresholds ranging from 11.3±3.5 cmH₂O at 10% of PEmax up to 56.6±17.2 cmH₂O at 50% of PEmax. The presence of low maximal pressure test values in this population is more likely due to sub- maxinial volitional effort rather than respiratory muscle weakness.

[0085] Correlation analysis between fSE parameters and mean PImo (Fig. 3a) or expiratory load (Fig. 3b) revealed strong relationships. Participant-aggregated correlations between mean PImo and fSE sRMGL o=0.84), fSE sRMGR (p=0.87), and fSE sEMG ( / ?=0.71) are shown in Fig. 3, subpanel (a). In a similar analysis, participant-aggregated correlations between expiratory load and fSE sRMGL (p=0.82), fSE sRMGR ( =0.79), and fSE sEMG ( =0.78) are visualized in Fig. 3, subpanel (b). After the fSE parameters were log-transformed, repeated -measures correlation analysis resulted in strong correlations between mean PImo and logtfSE sRMGL) (rrm=0.78), log(fSE sRMGR) (rrm=0.76), and logt fSE sEMG) (rrm=0.62), as depicted in Fig. 3, subpanel (c). Correlations of comparable magnitude were observed between expiratory load and log(fSE sRMGL) (rrm=0.77), log(fSE sRMGR) (rrm=0.74), and log(fSE sEMG) (rrm=0.62), shown in Fig. 3, subpanel (d).Attorney Docket No. 10034-429W012025-226

[0086] This is the first study, to the best of our knowledge, to measure sRMG and sEMG signals for quantification of respiratory muscle activity with a wearable device. Prior studies have used bench-top accelerometers and biopotential AFEs to measure equivalent signals from the parasternal and lower intercostal spaces (Blanco-Almaz´an et al., 2021; Lozano-Garc'ia et al., 2021). Notably, we observed strong correlations between both inspiratory fSE sRMGL and fSE sRMGR parameters with PImo without normalization to maximal efforts, which increases the burden of such measurements (Lozano-Garc'ia et al., 2019). Further, repeated measures correlation analysis revealed that the log-transformed fSE sRMG parameters derived during inspiration exhibited a strong overall association with respiratory mechanical output, reinforcing their potential as reliable indicators of inspiratory effort without calibration. When paired with the fSE sEMG results, which exhibited notable correlations across inspiratory loading conditions, neural respiratory drive and indices of neuromechanical coupling can be noninvasively quantified (Lozano- Garc'ia et al., 2021).

[0087] sRMG and sEMG signals have scarcely been researched under expiratory- loading conditions, as expiration is largely done in a passive manner by elastic recoil with notable expiratory muscle activity occurring primarily during active expiration. Thus, the finding of correlations between fSE sRMGL, fSE sRMGR, and fSE sEMG with expiratory- load during the incremental expiratory loading protocol is especially noteworthy. While fSE sRMG and fSE sEMG values were consistently lower during expiratory loading compared to inspiratory loading--accompanied by slightly lower correlations for all fSE parameters — clear patterns of increasing expiratory muscle activity were evident in both signals. The lower sRMG and sEMG amplitudes observed during expiratory loading compared to inspiratory loading are expected under the hypothesis that the observed activity originates from muscles such as the triangularis sterni or internal interosseous intercostals (De Troyer et al., 2005b). Given the greater source-separation from these expiratory muscles, which are further removed from the surface of the skin than the parasternal intercostals, it is reasonable to assume that sEMG and sRMG signal-to-noise ratio (SNR) would be lower. Future studies should seek to better understand the origin of such expiratory activity and determine its utility in the field of cardiopulmonary monitoring.

[0088] Time-frequency representations for detection of phase- specific respiratory muscle activityAttorney Docket No. 10034-429W012025-226

[0089] Representative sRMG and sEMG signals and spectrograms are shown for inspiratory (Fig. 4, subpanel (a)) and expiratory (Fig. 4, subpanel (b)) loading conditions. The spectrograms depict the cardiogenic components of the sEMG and sRMG signals, which are clearly distinguishable from the respiratory muscle activity. These plots also illustrate the fusion of respiratory phase context from the II’ waveform with sRMG and sEMG signals, which is fundamental to stratifying respiratory muscle activity within each respective phase.

[0090] Boxplots for pairwise comparisons between inspiratory and expiratory band¬ powers for sRMGR, sRMGL, and sEMG are shown for incremental inspiratory and expiratory loading sessions. All comparisons between inspiratory and expiratory band¬ powers for the sRMGR and sRMGL signals were significant (p < 0.001). sEMG band- power differed significantly across respiratory phases for all inspiratory loads, but only for the upper three (>=30% PEmax) expiratory loads. Notably, these findings highlight the sensitivity of the reSPIRE system, as sRMG can reliably detect elevated muscle activity even at airway resistances as low as 10% of maximal effort. The detection of such imbalances in inspiratory and expiratory activity could be useful in conditions such as DMD, where expiratory muscles are seemingly compromised more than inspiratory muscles and expiratory weakness manifests early (Khirani et al., 2014; LoMauro et al., 2015).

[0091] Estimation of respiratory parameters

[0092] The regression and BA plot are shown in Fig. 6, subpanel (a). Across all participants, a wide dynamic range of TVs, from 0.13 up to 4.56 L, and RRs, from 5.5 up to 35.7 brpm, were observed in the breathing maneuvers section. For all participants, 83.3+5.3% of the breaths detected by the spirometer had an associated IP-derived breath that passed the signal quality indexing, resulting in an average of 70.0+10.2 breaths per participant and N=1260 breaths total. 'The IP -based TV estimates derived from the reSPIRE patch strongly agreed with the ground truth measurements, with an R2 of 0.91+0.07, MAE of 0.135+0.063 L, and MAPE of 10.86+4.13% across all participants. The BA analysis with repeated measures correction showed a mean bias of 0.059 L with 95% LoA of -0.336 to 0.454 L. Estimates of respiratory timings including RR (MAE: 0.27+0.17 brpm, MAPE: 2.04+1.16%), Tins (MAE: 147+81 ms, MAPE: 5.80+3.54%), and Texp (MAE: 139+67 ms, MAPE: 5.03+2.63%) demonstrated accurate tracking of reference timings.

[0093] The reSPIRE system achieved low TV estimation errors, with a lower MAPE than our previous work, despite the inclusion of spontaneous breathing, reduced calibrationAttorney Docket No. 10034-429W012025-226 constraints, and more lenient breath rejection in this study (Berkebile et al., 2021). The average MAPE of 10.86% aligns with the performance of other state-of-the-art wearablebased TV estimation methods (Monaco and Stefanini, 2021). A more expansive range of RRs were included in this work, yielding slightly higher respiratory timing errors than our prior study, though only an RR of 12 brpm was previously tested while seated. IP remains one of the most promising approaches for estimating TV, and the reSPIRE system is well- suited to further improve TV monitoring by leveraging multimodality to further improve accuracy and facilitate generalized models (Berkebile et al., 2023).

[0094] Dynamic cardiopulmonary’ response to cycling

[0095] Time-series of ventilation (VE), HR, PEP, and ICGC-amp, which depict the cardiopulmonary dynamics during the constant-load cycling and recovery period are presented in Fig. 6, subpanel (b). IP-based estimates of TV (MAE: 0.152+0.115 L, MAPE: 13.83+9.87%) and RR (MAE: 0.94+0.62 brpm, MAPE: 4.88+2.91%) remained accurate during cycling, though with some performance degradation compared to estimation during the stationary breathing maneuvers. This accuracy is further demonstrated in the similarity of the VE time-series derived from the spirometer and IP signals across the cycling and recovery periods. The HR trends illustrate the anticipated increase over the 5-minute cycling period, with a sharp drop at the cessation of the cycling. Similarly, the reduction in PEP, which was derived from both ICG and SCGL signals, during cycling depicts the heightened sympathetic activity and cardiac contractility, which was followed by a return to near baseline in the recovery period. Finally, ICGC-amp, a proxy of CO, exhibits the expected increase during cycling, due to increased demand for blood flow, followed by subsequent return to baseline. Despite the non-standard electrode configuration, identifiable ICG waveforms were reliably measured for all but one participant. These results highlight several key strengths of the reSPIRE system: its capability to track rapid changes in cardiopulmonary physiology, its convenient form factor for mid-activity use, and that the signal acquisition and processing pipeline is robust to the inherently higher levels of noise during activity. Clinically, the presented measures can be used for assessing cardiopulmonary responses during standardized exercise stress-testing (Shandhi et al., 2020; Albouaini et al., 2007) or for determining impaired ventricular function (Ganti et al., 2022; Packer et al., 2006).

[0096] The disclosed embodiments of the device comprise a combination of sensing capabilities for the mechanical aspects of both cardiovascular and pulmonary function. The sensing of cardio-mechanical signals through the device enables hemodynamic monitoring,Attorney Docket No. 10034-429W012025-226 such as sensing changes in filling pressures (e.g., pulmonary capillary wedge pressure) and cardiac output, while the sensing of mechanical aspects of pulmonary function enables the concurrent assessment of respiratory workload from the intercostal muscles together with tidal volume and respiratory rate. For example, the cardio-mechanical signals can be used to derive information related to the pumping of the heart, as well as the timings of the cardiac cycle (e.g., providing timings for aortic valve opening and closing events) and thereby can allow the user to quantify left ventricular performance and function. There are known relationships between the pre-ejection period (PEP) and left ventricular ejection time (LVET) of the heart and ejection fraction, for example, with the ratio of PEP to LVET being inversely related to ejection fraction. Additionally, it is possible to analyze the timing and amplitude characteristics of the SCG signal to be able to extract hemodynamic parameters such as stroke volume, and to examine the signal in the diastolic portion of the heartbeat to extract filling characteristics such as pulmonary capillary wedge pressure. In many cases, the signal first has to be examined through signal quality indexing (SQI) approaches to remove the noise and artifacts from the waveform prior to further processing operations. Similarly, the PPG signal together with the SCG can yield information regarding peripheral vascular tone, and thereby can provide information on autonomic state and other parameters related to cardiac performance. As another example, the same accelerometer on the chest that yields SCG signals can be analyze in the lower frequencies (specifically in frequencies below the heartrate) to yield information related to the movements of the chest wall in response to breathing. These chest wall movements can indicate the degree to which the chest is expanding and contracting with each breath, and then the resultant tidal volume for the breath can be analyzed through the impedance signals to understand the relationship between respiratory effort and resultant ventilation. In a similar manner, the impedance signals measured at the chest by the device can both yield information related to hemodynamics (the flow of blood through the thoracic cavity, and in and out of the thoracic cavity can be used to estimate stroke volume as well as local blood volume pulse in the volume being interrogated by the sensors) and information related to respiration (the movement of air in and out of the lungs leads to changes in impedance, since air is less conductive than other biological tissues being interrogated by the sensor). Through these combined characteristics, the device enables a powerful window into understanding cardiopulmonary coupling, which can be applicable to many clinical problems including triage of patients in the emergency department (e.g., with dyspnea), monitoring of patients with asthma, monitoring of sleep quality for home sleep tests and / Attorney Docket No. 10034-429W012025-226 or advanced monitoring capabilities in sleep lab settings, etc. For example, disturbances in cardiopulmonary coupling such as increased respiratory effort without increasing tidal volume leading to greater variability from beat to beat in cardiac hemodynamics (stroke volume, PCWP, ejection fraction, etc.) could indicate that high intrapleural pressures are developing due to some obstruction (e.g., in sleep apnea and sleep disordered breathing). Additionally, pneumonia or other restrictive pulmonary diseases may lead to a reduction in tidal volume similarly but without such increases in intrapleural pressure and thereby limited effect on the cardiovascular hemodynamic variables. Such sensing approaches would normally require multiple devices to be placed on the body, which is less desirable for patients and caregivers than having one centrally placed device with duality of sensing modes with the same sensors being strategically selected for those purposes.

[0097] The placement of the device is such that the main module generally sits at the sternum for optimal seismocardiogram (cardio-mechanical) signal detection, while the wings generally sit on the intercostal muscles to capture the local muscle physiology during inspiration and expiration. The combination of global (tidal volume, respiratory rate) pulmonary physiology with local intercostal muscle physiology (respiratory effort in inspiratory and / or expiratory phases) is a unique aspect of the technology. The use of photoplethysmogram (PPG) sensing at the intercostal muscles, rather than usual placements at the fingertip, earlobe, toe, or other peripheral sites on the body, is novel in combination with the other sensing modalities as it improves the ability to detect local muscle activation in the intercostal muscles associated with increased respiratory workload. For many applications, including asthma, sleep, and COPD monitoring, the ability to quantify how hard the respiratory muscles in the chest are working during inspiration at expiration, coupled with the volume of air being inspired and expired with each breath, is valuable from a clinical standpoint. Again, the duality of sensing capability, capturing both local physiology and global physiology with the same sensing system is advantageous from a usability standpoint, but also can lead to a greater amount of information being drawn from the same location on the chest than with existing conventional approaches. The use of the PPG at the intercostal muscles for example represents a novel approach when supplemented with the impedance measurement across the chest. The use of the impedance with PPG could, for example, differentiate the blood volume pulse changes due to increased blood flow to the intercostal muscles as compared to increase blood flow overall in the chest cavity.Attorney Docket No. 10034-429W012025-226

[0098] The dual-purpose usage of some of the sensors to provide both cardiac and respiratory information based on signal settings in the firm ware of the embedded system is also a unique strength of the approach. For example, the accelerometer signal from the chest includes both SCG measurement capability to capture hemodynamics, but also low frequency chest wall movement to understand the respiratory induced expansion and contraction of the chest cavity; the impedance signal includes both a larger amplitude low frequency component indicative of tidal volume and a smaller amplitude higher frequency component indicative of the thoracic blood volume shifts with each heartbeat; the PPG signals from the chest indicate the peripheral blood volume pulse arrival to a distal location which can be used with the SCG timings to extract pulse transit time and thereby information on peripheral vascular tone, and also can be monitored on a slower time scale to extract the increase in blood flow to the intercostal muscles with increased respiratory- workload. The multiple modalities leveraged by the device can allow for automated mode switching by truly understanding the context and physiological state of the human, and thereby triggering more power hungry but also more powerful sensing approaches from the sensors deployed on the device. For example, if the device detects that there might be a higher risk of clinical deterioration in a patient wearing the device in the hospital, then a full sensing mode can be employed where all signals are turned on and captured to providing essential monitoring capability; once the device detects that this deterioration risk may have been a false alarm, the sensing modes can be sequentially turned off to save power and increase battery life. Such approaches are only possible with this device because of the multiple modalities of sensing and specifically the dual cardiopulmonary sensing capability which is essential to capture clinical state of patients in many hospital settings and applications.

[0099] Computing Environment

[0100] When the logical operations described herein are implemented in software, the process may execute on any type of computing architecture or platform. For example, referring to FIG. 7, an example computing device upon which embodiments of the invention may be implemented is illustrated. In particular, at least one processing device described above may be a computing device, such as computing device 1000 shown in FIG.7. For example, computing device 1000 may be a component of the cloud computing and storage system. Computing device 1000 may comprise all or a portion of server. The computing device 1000 may include a bus or other communication mechanism for communicating information among various components of the computing device 1000. InAttorney Docket No. 10034-429W012025-226 its most basic configuration, computing device 1000 typically includes at least one processing unit 1006 and system memory 1004. Depending on the exact configuration and type of computing device, system memory 1004 may be volatile (such as random access memory (RAM)), non-volatile (such as read-only memory (ROM), flash memory, etc.), or some combination of the two. This most basic configuration is illustrated in FIG. 7 by dashed line 1002. The processing unit 1006 may be a standard programmable processor that performs arithmetic and logic operations necessary for operation of the computing device 1000.

[0101] Computing device 1000 may have additional features / functionality. For example, computing device 1000 may include additional storage such as removable storage 1008 and non-removable storage 1010 including, but not limited to, magnetic or optical disks or tapes. Computing device 1000 may also contain network connection(s) 1016 that allow the device to communicate with other devices. Computing device 1000 may also have input device(s) 1014 such as a keyboard, mouse, touch screen, scanner, etc. Output device(s) 1012 such as a display, speakers, printer, etc. may also be included. The additional devices may be connected to the bus in order to facilitate communication of data among the components of the computing device 1000. All these devices are well known in the art and need not be discussed at length here. Though not shown in FIG. 7, in some instances computing device 1000 includes an interface. The interface may include one or more components configured to transmit and receive data via a communication network, such as the Internet, Ethernet, a local area network, a wide-area network, a workstation peer-to-peer network, a direct link network, a wireless network, or any other suitable communication platform. For example, interface may include one or more modulators, demodulators, multiplexers, demultiplexers, network communication devices, wireless devices, antennas, modems, and any other type of device configured to enable data communication via a communication network. Interface may also allow the computing device to connect with and communicate with an input or an output peripheral device such as a scanner, printer, and the like.

[0102] The processing unit 1006 may be configured to execute program code encoded in tangible, computer-readable media. Computer-readable media refers to any media that is capable of providing data that causes the computing device 1000 (i.e,, a machine) to operate in a particular fashion. Various computer-readable media may be utilized to provide instructions to the processing unit 1006 for execution. Common forms of computer-readable media include, for example, magnetic media, optical media, physicalAttorney Docket No. 10034-429W012025-226 media, memory chips or cartridges, a carrier wave, or any other medium from which a computer can read. Example computer-readable media may include, but is not limited to, volatile media, non-volatile media and transmission media. Volatile and non-volatile media may be implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data and common forms are discussed in detail below. Transmission media may include coaxial cables, copper wires and / or fiber optic cables, as well as acoustic or light waves, such as those generated during radio-wave and infra-red data communication. Example tangible, computer-readable recording media include, but are not limited to, an integrated circuit (e.g., field-programmable gate array or application- specific IC), a hard disk, an optical disk, a magneto-optical disk, a floppy disk, a magnetic tape, a holographic storage medium, a solid-state device, RAM, ROM, electrically erasable program read-only memory (EEPROM), flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices.

[0103] In an example implementation, the processing unit 1006 may execute program code stored in the system memory 1004. For example, the bus may carry data to the system memory 1004, from which the processing unit 1006 receives and executes instructions. The data received by the system memory 1004 may optionally be stored on the removable storage 1008 or the non-removable storage 1010 before or after execution by the processing unit 1006.

[0104] Computing device 1000 typically includes a variety of computer-readable media. Computer-readable media can be any available media that can be accessed by device 1000 and includes both volatile and non-volatile media, removable and non¬ removable media. Computer storage media include volatile and non-volatile, and removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. System memory 1004, removable storage 1008, and non-removable storage 1010 are all examples of computer storage media. Computer storage media include, but are not limited to, RAM, ROM, electrically erasable program read-only memory (EEPROM), flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desiredAttorney Docket No. 10034-429W012025-226 information and which can be accessed by computing device 1000. Any such computer storage media may be part of computing device 1000.

[0105] Conclusion

[0106] The construction and arrangement of the systems and methods, as shown in the various implementations, are illustrative only. Although only a few implementations have been described in detail in this disclosure, many modifications are possible (e.g., variations in sizes, dimensions, structures, shapes, proportions of the various elements, values of parameters, mounting arrangements, use of materials, colors, orientations, etc.). For example, the position of elements may be reversed or otherwise varied, and the nature or number of discrete elements or positions may be altered or varied. Accordingly, all such modifications are intended to be included within the scope of the present disclosure. The order or sequence of any process or method steps may be varied or re-sequenced according to alternative implementations. Other substitutions, modifications, changes, and omissions may be made in the design, operating conditions, and arrangement of the implementations without departing from the scope of the present disclosure.

[0107] The present disclosure contemplates methods, systems, and program products on any machine-readable media for accomplishing various operations. The implementations of the present disclosure may be implemented using existing computer processors, or by a special purpose computer processor for an appropriate system, incorporated for this or another purpose, or by a hardwired system. Implementations within the scope of the present disclosure include program products, including machine-readable media for carrying or having machine-executable instructions or data structures stored thereon. Such machine-readable media can be any available media that can be accessed by a general-purpose or special-purpose computer or other machine with a processor. By way of example, such machine-readable media can comprise RAM, ROM, EPROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to carry or store desired program code in the form of machine-executable instructions or data structures, and which can be accessed by a general purpose or special purpose computer or other machine with a processor.

[0108] When information is transferred or provided over a network or another communications connection (either hardwired, wireless, or a combination of hardwired or wireless) to a machine, the machine properly views the connection as a machine-readable medium; thus, any such connection is properly termed a machine-readable medium. Combinations of the above are also included within the scope of machine-readable media.Attorney Docket No. 10034-429W012025-226 Machine-executable instructions include, for example, instructions and data that cause a general-purpose computer, special-purpose computer, or special-purpose processing machine to perform a certain function or group of functions.

[0109] It is to be understood that the methods and systems are not limited to specific synthetic methods, specific components, or to particular compositions. It is also to be understood that the terminology used herein is for the purpose of describing particular implementations only and is not intended to be limiting.

[0110] As used in the specification and the appended claims, the singular forms “a,” “an” and “the” include plural referents unless the context clearly dictates otherwise. Ranges may be expressed herein as from “about” one particular value, and / or to “about” another particular value. When such a range is expressed, another implementation includes from the one particular value and / or to the other particular value. Similarly, when values are expressed as approximations, by use of the antecedent “about,” it will be understood that the particular value forms another implementation. It will be further understood that the endpoints of each of the ranges are significant both in relation to the other endpoint, and independently of the other endpoint.

[0111] “Optional” or “optionally” means that the subsequently described event or circumstance may or may not occur and that the description includes instances where said event or circumstance occurs and instances where it does not.

[0112] Throughout the description and claims of this specification, the word “comprise” and variations of the word, such as “comprising” and “comprises,” means “including but not limited to,” and is not intended to exclude, for example, other additives, components, integers or steps. “Exemplary” means “an example of’ and is not intended to convey an indication of a preferred or ideal implementation. “Such as” is not used in a restrictive sense but for explanatory purposes.

[0113] Disclosed are components that can be used to perform the disclosed methods and systems. These and other components are disclosed herein, and it is understood that when combinations, subsets, interactions, groups, etc. of these components are disclosed while specific reference of each various individual and collective combinations and permutation of these may not be explicitly disclosed, each is specifically contemplated and described herein, for all methods and systems. This applies to all aspects of this application, including, but not limited to, steps in disclosed methods. Thus, if there are a variety of additional steps that can be performed it is understood that each of these additional stepsAttorney Docket No. 10034-429W012025-226 can be performed with any specific implementation or combination of implementations of the disclosed methods.

[0114] The following patents, applications, and publications, as listed below and throughout this document, are hereby incorporated by reference in their entirety herein.Reference List #1[1] Albouaini, K., Egred, M., Alahmar, A., Wright, D. J., 2007. Cardiopulmonary exercise testing and its application. Postgraduate Medical Journal 83, 675-682. doi: 10.1136 / hrt.2007.121558.[2] Arbol, J. R., Perakakis, P., Garrido, A., Mata, J. L., Fernandez-Santaella, M. C., Vila, J., 2017. Mathematical ' detection of aortic valve opening (B point) in impedance cardiography: A comparison of three popular algorithms. Psychophysiology 54, 350- 357. doi:10.1111 / psyp.l2799.[3] Ashouri, H., Hersek, S., Inan, O. T., 2018. Universal Pre-Ejection Period Estimation Using Seismocardiography: Quantifying the Effects of Sensor Placement and Regression Algorithms. IEEE Sensors Journal 18, 1665-1674. doi:10.1109 / JSEN.2017.2787628.[4] Bakdash, J. Z., Marusich, L. R., 2017. Repeated Measures Correlation. Front. Psychol. 8.doi: 10. 389 / fpsyg.2017.00456.[5] Berkebile, J. A., Gazi, A. H., Chan, M., Albarran, T. D., Rozell, C. J., Inan, O. T., Beach, P. A., 2025. Remote Monitoring of Cardiovascular Autonomic Dysfunction in Synucleinopathies With a Wearable Chest Patch. IEEE Sensors Journal 25, 7250-7262. doi: 10.1109 / JSEN.2024.3523849.[6] Berkebile, J. A., Mabrouk, S., Ganti, V., Srivatsa, A., Sanchez-Perez, J., Inan, O., 2021.Towards Estimation of Tidal Volume and Respiratory Timings via Wearable-Patch- Based Impedance Pneumography in Ambulatory Settings. IEEE Transactions on Biomedical Engineering 69, 1909-1919. doi:10.1109 / TBME.2021.3130540.[7] Berkebile, J. A., Sanchez-Perez, J. A., Ozmen, G. C., Inan, O. T., 2023. Improving Tidal Volume Estimation via Fusion of Impedance Pneumography and Accelerometry, in: 2023 IEEE SENSORS, pp. 1-4. doi:10.1109 / SENSORS56945.2023.10324945.[8] Blanco- Almazan, D., Groenendaal, W., Catthoor, F., Jane, R., 2019. Chest Movement and Respiratory Volume both Contribute to Thoracic Bioimpedance during Loaded Breathing. Scientific Reports 9, 1-11. doi:10.1038 / s41598-019-56588-4.[9] Bianco-Almazan, D., Groenendaal, W., Lozano-Garcia, M., Estrada-Petrocelli, L., Lijnen, L., Smeets, C., Ruttens, D., Catthoor, F., Jane, R., 2021. Combining Bioimpedance and Myographic Signals for the Assessment of COPD During Loaded Breathing. IEEEAttorney Docket No. 10034-429W012025-226 Transactions on Biomedical Engineering 68, 298- 307. doi: 10.1109 / TBME.2020.2998009.

[0010] Bland, J. M., Altman, D. G., 2007. Agreement Between Methods of Measurement with Multiple Observations Per Individual. Journal of Biopharmaceutical Statistics 17, 571— 582. doi: 10.1080 / 10543400701329422.

[0011] Buddhe, S., Cripe, L., Friedland-Little, J., Kertesz, N., Eghtesady, P., Finder, J., Hor, K., Judge, D. P., Kinnett, K., McNally, E. M., Raman, S., Thompson, W. R., Wagner, K. R., Olson, A. K., 2018. Cardiac Management of the Patient With Duchenne Muscular Dystrophy. Pediatrics 142, S72-S81. doi: 10.1542 / peds.2018-03331.

[0012] Burton, D. A., Stokes, K., Hall, G. M., 2004. Physiological effects of exercise. Continuing Education in Anaesthesia Critical Care & Pain 4, 185-188. doi: 10.1093ZbjaceaccpZmkh050.

[0013] Caldwell, A., 2022. SimplyAgree: An R package and jamovi Module for Simplifying Agreement and Reliability Analyses. Journal of Open Source Software 7, 4148. doi: 10.21105 / joss.04148.

[0014] Chan, M„ Ganti, V. G., Heller, J. A., Abdallah, C. A., Etemadi, M„ Inan, O. T., 2021.Enabling Continuous Wearable Reflectance Pulse Oximetry at the Sternum. Biosensors 2021, Vol. 11, Page 521 11, 521. doi: 10.3390 / BIOS 11120521.

[0015] Charlton, P. H., Bonnici, T., Tarassenko, L., Clifton, D. A., Beale, R., Watkinson, P. J., Alastruey, J., 2021. An impedance pneumography signal quality index: Design, assessment, and application to respiratory rate monitoring. Biomedical Signal Processing and Control 65, 102339. doi: 10.1016Zj.bspc.2020.102339.

[0016] De Troyer, A., Boriek, A. M., 2011. Mechanics of the Respiratory Muscles, in: Prakash, Y. S. (Ed.), Comprehensive Physiology. 1 ed.. Wiley, pp. 1273-1300. doi: 10.1002 / cphy.cl00009.

[0017] De Troyer, A., Kirkwood, P. A., Wilson, T. A., 2005a. Respiratory Action of the Intercostal Muscles. Physiological Reviews 85, 717-756. doi: 10.1152Zphysrev.00007.2004.

[0018] De Troyer, A., Kirkwood, P. A., Wilson, T. A., 2005b. Respiratory Action of the Intercostal Muscles. Physiological Reviews 85, 717-756. doi: 10.1152 / physrev.00007.2004.

[0019] Doorduin, J., van Hees, H. W. H., van der Hoeven, J. G., Heunks, L. M. A., 2013.Monitoring of the Respiratory Muscles in the Critically Ill. Am J Respir Crit Care Med 187, 20-27. doi:10.1164Zrccm.201206-1117CP.Attorney Docket No. 10034-429W012025-226

[0020] Ernst, J. M., Litvack, D. A., Lozano, D. L., Cacioppo, J. T., Berntson, G. G., 1999.Impedance pneumography: Noise as signal in impedance cardiography. Psychophysiology 36, 333-338. doi:l0.l017 / S004857729998l003.

[0021] Estrada, L., Torres, A., Sarlabous, L., Jane, R., 2016. Improvement in Neural Respiratory Drive Estimation From Diaphragm Electromyographic Signals Using Fixed Sample Entropy. IEEE Journal of Biomedical and Health Informatics 20, 476^-85. doi: 10.1109 / JBHI.2015.2398934.

[0022] Estrada, L., Torres, A., Sarlabous, L., Jane, R., 2017. Influence of Parameter Selection in Fixed Sample Entropy of Surface Diaphragm Electromyography for Estimating Respiratory Activity. Entropy 19, 460. doi: 10.3390 / el 9090460.

[0023] Evans, J. A., Whitelaw, W. A., 2009. The Assessment of Maximal Respiratory Mouth Pressures In Adults. Respiratory Care 54, 1348-1359.

[0024] Frerichs, I., Vogt, B., Wacker, J., Paradiso, R., Braun, F., Rapin, M., Caldani, L„ Chetelat, O., Weiler, N., 2020. Multimodal remote chest monitoring system with wearable sensors: A validation study in healthy subjects. Physiol. Meas. 41, 015006. doi: 10.1088 / 1361-6579 / ab668f.

[0025] Ganti, V., Carek, A. M., Jung, H., Srivatsa, A. V., Cherry, D., Johnson, L. N., Inan, O. T., 2021a. Enabling Wearable Pulse Transit Time-Based Blood Pressure Estimation for Medically Underserved Areas and Health Equity: Comprehensive Evaluation Study. JMIR mHealth and ullealth 9, e27466. doi: 10.2196 / 27466.

[0026] Ganti, V. G., Carek, A. M., Nevius, B. N., Heller, J. A., Etemadi, M., Inan, O. T., 2021b.Wearable Cuff-Less Blood Pressure Estimation at Home via Pulse Transit Time. IEEE Journal of Biomedical and Health Informatics 25, 1926-1937. doi: 10.1109 / JBHI.2020. 021532.

[0027] Ganti, V. G., Gazi, A. H., An, S., Srivatsa, A. V., Nevius, B. N., Nichols, C. L, Carek, A. M., Fares, M., Abdulkarim, M., Hussain, T., Greil, F. G., Etemadi, M., Inan, O. T., Tandon, A., 2022. Wearable Seismocardiography-Based Assessment of Stroke Volume in Congenital Heart Disease. Journal of the American Heart Association 11, e026067. doi: 10.1161 / 1 AHA.122.026067.

[0028] Hallett, S., Toro, F., Ashurst, J. V., 2025. Physiology, Tidal Volume, in: StatPearls.StatPearls Publishing, Treasure Island (FL).

[0029] Hudson, A. L., Luu, B. L., Gandevia, S. C., Butler, J. E., 2024. Graded onset of parasternal intercostal inspiratory activity detected with surface electromyography in healthyAttorney Docket No. 10034-429W012025-226 young females and males. Journal of Applied Physiology 136, 695-706. doi: 10.1152 / japplphysiol.00604.2023.

[0030] Inan, O. T., Migeotte, P. F., Park, K. S., Etemadi, M., Tavakolian, K., Casanella, R., Zanetti, J., Tank, J., Funtova, I., Prisk, G. K., Di Rienzo, M., 2015. Ballistocardiography and Seismocardiography: A Review of Recent Advances. IEEE Journal of Biomedical and Health Informatics 19, 1414-1427. doi:10.1109 / JBHI.2014.2361732.

[0031] Ishikawa, Y., Miura, T., Ishikawa, Y., Aoyagi, T., Ogata, IL, Hamada, S., Minami, R., 2011. Duchenne muscular dystrophy: Survival by cardio-respiratory interventions. Neuromuscular Disorders 21, 47-51. doi:10.1016 / j.nmd.2010.09.006.

[0032] Khirani, S., Ramirez, A., Aubertin, G., Boule, M., Chemouny, C., Forin, V., Fauroux, B., 2014. Respiratory muscle decline in duchenne muscular dystrophy. Pediatric Pulmonology 49, 473-481. doi:10.1002 / ppul.22847.

[0033] Kleiger, R. E., Stein, P. K., Bigger, J. T., 2005. Heart rate variability: Measurement and clinical utility. Annals of Noninvasive Electrocardiology 10, 88—101. doi:10.1111 / j.1542 -474X.2005.10101.x

[0034] Klum, M., Schenck, T., Pielmus, A., Tigges, T., Orglmeister, R., 2018. Short distance impedance pneumography. Current Directions in Biomedical Engineering 4, 109-113. doi: 10.1 15 / cdbme-2018-0028.

[0035] Klum, M., Urban, M., Tigges, T., Pielmus, A. G., Feldheiser, A., Schmitt, T., Orglmeister, R., 2020. Wearable cardiorespiratory monitoring employing a multimodal digital patch stethoscope: Estimation of ecg, pep, Ivetand respiration using a 55 mm single-lead ecg and phonocardiogram. Sensors (Switzerland) 20, 2033. doi:10.3390 / s20072033.

[0036] Kuznetsova, A., Brocklioff, P. B., Christensen, R. H. B., 2017. ImerTest Package: Tests in Linear Mixed Effects Models. Journal of Statistical Software 82, 1-26. doi:10.18637 / jss.v082.il.

[0037] Lee, S., Grundlehner, B., van der Westen, R. G., Polito, S., Van Hoof, C., 2019.Nightingale V2: Lowpower Compact-sized Multi-Sensor Platform for Wearable Health Monitoring, in: 201941st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 1290- 1293. doi: 10.1109 / EMBC.2019.8856847.

[0038] Lewis, R. P., Rittgers, S. E., Forester, W. F., Boudoulas, H., 1977. A critical review of the systolic time intervals. Circulation 56, 146-158. doi: 10.1161 / 01. CIR.56.2.146.Attorney Docket No. 10034-429W012025-226

[0039] Lin, L., Guan, L., Wu, W., Chen, R., 2019. Correlation of surface respiratory electromyography with esophageal diaphragm electromyography. Respiratory Physiology & Neurobiology 259, 45-52. doi: 10.1016 / j.resp.2018.07.004.

[0040] LoMauro, A., D’Angelo, M. G., Aliverti, A., 2.015. Assessment and management of respiratory function in patients with Duchenne, muscular dystrophy: Current and emerging options. Therapeutics and Clinical Risk Management. 11, 1475-1488. doi: 10.2147 / TCRM. S55889.[41 j Lozano-Garcia, M., Estrada-Petrocelli, L., Blanco-Almazan, D., Tas, B., Cho, P. S. P., Moxham, J., Rafferty, G. F., Torres, A., Jane, R., Jolley, C. J., 2022. Noninvasive Assessment of Neuromechanical and Neuroventilatory Coupling in COPD. IEEE Journal of Biomedical and Health Informatics 26, 3385-3396. doi: 10.1109 / JBH1.2022.3166255.

[0042] Lozano-Garcia, M., Estrada-Petrocelli, L., Moxham, J., Rafferty, G. F., Torres, A., Jolley, C. J., Jane, R., 2019. Noninvasive Assessment of Inspiratory Muscle Neuromechanical Coupling During Inspiratory Threshold Loading. IEEE Access 7, 183634-183646. doi: 10.1109 / ACCESS.2019.2960077.

[0043] Lozano-Garcia, M., Estrada-Petrocelli, L., Torres, A., Rafferty, G. F., Moxham, J., Jolley, C. J., Jane, R., 202.1. Noninvasive Assessment of Neuromechanical Coupling and Mechanical Efficiency of Parasternal Intercostal Muscle during Inspiratory Threshold Loading. Sensors 21, 1781. doi: 10.3390 / s21051781.

[0044] Lozano-Garcia, M., Sarlabous, L., Moxham, J., Rafferty, G. F., Torres, A., Jane, R., Jolley, C. J., 2018. Surface mechanomyography and electromyography provide noninvasive indices of inspiratory muscle force and activation in healthy subjects. Sei Rep 8, 16921. doi:10.1038 / s41598-018-35024-z.

[0045] Martin, S. S., Aday, A. W., Almarzooq, Z. I., Anderson, C. A., Arora, P., Avery, C. L., Baker-Smith, C. M., Barone Gibbs, B., Beaton, A. Z., Boehme, A. K., Commodore- Mensah, Y., Currie, M. E., Elkind, M. S., Evenson, K. R., Generoso, G., Heard, D. G., Hiremath, S., Johansen, M. C., Kalani, R., Kazi, D. S., Ko, D., Liu, J., Magnani, J. W., Michos, E. D., Mussolino, M. E., Navaneethan, S. D., Parikh, N. I., Perman, S. M., Poudel, R., Rezk-Hanna, M., Roth, G. A., Shah, N. S., St-Onge, M. P., Thacker, E. L., Tsao, C. W., Urbut, S. M., Van Spall, H. G., Voeks, J. H., Wang, N. Y., Wong, N. D., Wong, S. S., Yaffe, K., Palaniappan, L. P., on behalf of the American Heart Association Council on Epidemiology and Prevention Statistics Committee and Stroke Statistics Subcommittee, 2024. 2024 Heart Disease and Stroke Statistics: A Report of US andAttorney Docket No. 10034-429W012025-226 Global Data From the American Heart Association. Circulation 149, e347-e913. doi: 10.1161 / CIR.0000000000001209.

[0046] Mercuri, E., Bonnemann, C. G., Muntoni, F., 2019. Muscular dystrophies. The Lancet 394, 2025-2038. doi:10.1016 / S0140-6736(19)32910-l.

[0047] Monaco, V., Stefanini, C., 2021. Assessing the Tidal Volume through Wearables: A Scoping Review. Sensors 21, 4124. doi:10.3390 / s21124124.

[0048] Murphy, P. B., Kumar, A., Reilly, C., Jolley, C., Walterspacher, S., Fedele, F., Hopkinson, N. S., Man, W. D. C., Polkey, M. I., Moxham, J., Hart, N., 2011. Neural respiratory drive as a physiological biomarker to monitor change during acute exacerbations of COPD. Thorax 66, 602-608. doi:10.1136 / thx.2010.151332.

[0049] Packer, M., Abraham. W. T., Mehra, M. R., Yancy, C. W., Lawless, C. E., Mitchell, J. E., Smart, F. W., Bijou, R„ O’Connor, C. M., Massie, B. M., Pina, I. L., Greenberg, B. H., Young, J. B., Fishbein, D. P., Hauptman, P. J., Bourge, R. C., Strobeck, J. E., Murali, S., Schocken, D., Teerlink, J. R., Levy, W. C., Trupp, R. J., Silver, M. A., 2006. Utility of Impedance Cardiography for the Identification of Short-Term Risk of Clinical Decompensation in Stable Patients With Chronic Heart Failure. Journal of the American College of Cardiology 47, 2245-2252. doi:10.1016 / j.jacc.2005.12.071.

[0050] Peppard, P. E., Young, T., Barnet, J. H., Palta, M., Hagen, E. W., Hla, K. M., 2013.Increased Prevalence of Sleep-Disordered Breathing in Adults. American Journal of Epidemiology 177, 1006-1014. doi:10.1093 / aje / kws342.

[0051] Pessoa, I. M. S., Parreira, V. F., Fregonezi, G. A., Sheel, A. W., Chung, F., Reid, W. D., 2014. Reference Values for Maximal Inspiratory Pressure: A Systematic Review. Canadian Respiratory Journal 21, 982374. doi:10.1155 / 2014 / 982374.

[0052] Pilz, N., Patzak, A., Bothe, T. L., 2023. The pre-ejection period is a highly stress dependent parameter of paramount importance for pulse-wave-velocity based applications. Front. Cardiovasc. Med. 10. doi:10.3389 / fcvm.2023.1138356.

[0053] Ramsook, A. H., Mitchell, R. A., Bell, T., Calli, S., Kennedy, C., Lehmann, J., Thompson, M., Puyat, J. H., Guenette, J. A., 2017. Is parasternal intercostal EMG an accurate surrogate of respiratory neural drive and biomarker of dyspnea during cycle exercise testing? Respiratory Physiology & Neurobiology 242, 40-44. doi: 10.1016 / j.resp.2017.03.003.

[0054] Reisner, A., Shaltis, P. A., McCombie, D., Asada, H. H., 2008. Utility of the photopl ethysmogram in circulatory monitoring. Anesthesiology 108, 950-958. doi: 10.1097 / ALN.0b013e31816c89el.Attorney Docket No. 10034-429W012025-226

[0055] Rolfe, S., 2019. Rate Monitoring. British Jounral of Nursing 28, 504-508. Sanchez- Perez, J. A., Berkebile, J. A., Nevius, B. N., Ozmen, G. C., Nichols, C. J., Ganti, V. G., Mabrouk, S. A., Clifford, G. D., Kamaleswaran, R., Wright, D. W., Inan, O. T., 2022a. A Wearable Multimodal Sensing System for Tracking Changes in Pulmonary Fluid Status, Lung Sounds, and Respiratory Markers. Sensors 202.2, Vol. 22, Page 113022, 1130-1130. doi: 10.3390 / S22031130.

[0056] Sanchez-Perez, J. A., Mabrouk, S., Berkebile, J. A., Esper, A., Yang, P., Kamaleswaran, R., Inan, O. T., 2022b. Initial Validation of Multi-Frequency Patch-Based Impedance Pneumography in Hospital Settings, in: 2022 IEEE Sensors, pp. 1-4. doi: 10.1109 / SENSORS52175.2022.9967287.

[0057] Sarlabous, L., Torres, A., Fiz, J. A., Gea, J., Martinez-Llorens, J. M., Jane, R., 2009.Evaluation of the respiratory muscular function by means of diaphragmatic mechanomyographic signals in copd patients, in: 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 3925-3928. doi: 10.1109 / IEMBS.2009.5333536.

[0058] Sawnani, H., Thampratankul, L., Szczesniak, R. D., Fenchel, M. C., Simakajornboon, N., 2015. Sleep Disordered Breathing in Young Boys with Duchenne Muscular Dystrophy. The Journal of Pediatrics 166, 640-645. el. doi:10.1016 / j.jpeds.2014.12.006.

[0059] Schiff, G. D., Fung, S., Speroff, T., McNutt, R. A., 2003. Decompensated heart failure:Symptoms, patterns of onset, and contributing factors. The American Journal of Medicine 114, 625-630. doi:10.1016 / S0002- 9343(03)00132-3.[60 J Seppa, V. P., Viik, J., Hyttinen, J., 2010. Assessment, of pulmonary flow using impedance pneumography. IEEE Transactions on Biomedical Engineering 57, 2277-2285. doi: 10.1109 / TBME.2010.2051668.

[0061] Serhani, M. A., T. El Kassabi, H., Ismail, H., Nujum Navaz, A., 2020. ECG Monitoring Systems: Review, Architecture, Processes, and Key Challenges. Sensors 20, 1796. doi: 10.3390 / S20061796.

[0062] Shandhi, M. M. H., Fan, J., Heller, J. A., Etemadi, M., Klein, L., Inan, O. T., 2022.Estimation of Changes in Intracardiac Hemodynamics Using Wearable Seismocardiography and Machine Learning in Patients With Heart Failure: A Feasibility Study. IEEE Transactions on Biomedical Engineering 69, 2443-2455. doi: 10.1109 / TBME.2022.3147066.

[0063] Shandhi, M. M. H., Hersek, S., Fan, J., Sander, E., De Marco, T., Heller, J. A., Etemadi, M., Klein, L., Inan, O. T., 2020. Wearable Patch-Based Estimation of Oxygen UptakeAttorney Docket No. 10034-429W012025-226 and Assessment of Clinical Status during Cardiopulmonary Exercise Testing in Patients With Heart Failure. Journal of Cardiac Failure 26, 948-958. doi: 10.1016 / j. cardfail.2020.05.014.

[0064] Sherwood (Chair), A,, Allen, M. T., Fahrenberg, J., Kelsey, R. M., Lovallo, W. R., van Doomen, L. J., 1990. Methodological Guidelines for Impedance Cardiography. Psychophysiology 27, 1-23. doi: 10.1111 / j.1469-8986.199O.tbO2171.x.

[0065] Tanaka, IL, Monahan, K. D., Seals, D. R., 2001. Age -predicted maximal heart rate revisited. Journal of the American College of Cardiology 37, 153-156. doi: 10.1016 / 80735-1097(00)01054-8.

[0066] Viollet, L., Thrush, P. T., Flanigan, K. M., Mendell, J. R., Allen, H. D., 2012. Effects of AngiotensinConverting Enzyme Inhibitors and / or Beta Blockers on the Cardiomyopathy in Duchenne Muscular Dystrophy. The American Journal of Cardiology 110, 98-102. doi:10.1016 / j.amjcard.2012.02.064.

[0067] Winter, B., 2013. Linear models and linear mixed effects models in R with linguistic applications, doi: 10.48550 / arXiv.1308.5499, arXiv: 1308.5499.

[0068] Zia, J., Kimball, J., Hersek, S., Shandhi, M. M. IL, Semiz, B., Inan, O. T., 2020. A Unified Framework for Quality Indexing and Classification of Seismocardiogram Signals. IEEE Journal of Biomedical and Health Informatics 24, 1080-1092. doi: 10.1109 / JBHI.2019.2931348.Reference List #211 '] J. Askanazi, P. A. Silverberg, R. I. Foster, A. I. Hyman, J. Milic-Emili, and J. M. Kinney, “Effects of respiratory apparatus on breathing pattern,” https: / / doi. Org / 10.ri52 / jappl.1980.48.4.577, vol. 48, no. 4, pp. 577-580, 1980, doi: 10.1152 / JAPPL.1980.48.4.577.[2’] V. Monaco and C. Stefanini, “Assessing the Tidal Volume through Wearables: A Scoping Review,” Sensors, vol. 21, no. 12, Art. no. 12, Jan. 2021, doi: 10.3390 / s21124124.[3'] C. Massaroni, A. Nicolo, E. Schena, and M. Sacchetti, “Remote Respiratory Monitoring in the Time of COVID-19,” Front. Physiol., vol. 11, no. May, pp. 1-4, 2020, doi: 10.3389 / fphys.2020.0063.[4'] M. Lozano-Garcia et al., “Surface mechanomyography and electromyography provide non-invasive indices of inspiratory muscle force and activation in healthy subjects,” Sei. Rep., vol. 8, no. 1, Art. no. 1, Nov. 2018, doi: 10.1038 / s41598-018-35024-z.Attorney Docket No. 10034-429W012025-226 [5'] M. Centonze et al., “Cardiac-CT and cardiac-MR cost-effectiveness: a literature review,” Radiol. Med. (Torino), vol. 125, no. 11, pp. 1200-1207, Nov. 2020, doi: 10.1007 / sl 1547 -020-01290-z.[6'] J. A. Berkebile et al., “Remote Monitoring of Cardiovascular Autonomic Dysfunction in Synucleinopathies With a Wearable Chest Patch,” IEEE Sens. J., vol. 25, no. 4, pp.7250-7262, Feb. 2025, doi: 10.1109 / JSEN.2024.3523849.[7'] M. M. H. Shandhi et al., “Wearable Patch-Based Estimation of Oxygen Uptake and Assessment of Clinical Status during Cardiopulmonary Exercise Testing in Patients With Heart Failure,” J. Card. Fail., vol. 26, no. 11, pp. 948-958, 2020, doi: 10.1016 / j.cardfail.2020.05.014.[8'] V. G. Ganti et al., “Wearable Seismocardiography-Based Assessment of Stroke Volume in Congenital Heart Disease,” J. Am. Heart Assoc., vol. 11, no. 18, p. e026067, Sep.2022, doi: 10.1161 / JAHA.122.026067.[9'] M. Klum et al., “Wearable cardiorespiratory monitoring employing a multimodal digital patch stethoscope: Estimation of ecg, pep, Ivetand respiration using a 55 mm single¬ lead ecg and phonocardiogram,” Sens. Switz., vol. 20, no. 7, p. 2033, Apr. 2020, doi: 10.3390 / S20072033.[10'] K. S. Moon and S. Q. Lee, “A Wearable Multimodal Wireless Sensing System for Respiratory Monitoring and Analysis,” Sensors, vol. 23, no. 15, Art. no. 1, Jan. 2023, doi: 10.3390 / S23156790.[11'] I. Frerichs et al., “Multimodal remote chest monitoring system with wearable sensors: a validation study in healthy subjects,” Physiol. Meas., vol. 41, no. 1, p. 015006, Feb.2020, doi: 10.1088 / 1361 -6579 / ab668f.[12'] A. Sherwood] Chair), M. T. Allen, J. Fahrenberg, R. M. Kelsey, W. R. Lovallo, and L. J.P. van Doomen, “Methodological Guidelines for Impedance Cardiography,” Psychophysiology, vol. 27, no. 1, pp. 1-23, 1990, doi: 10.111 l / j.1469- 8986.1990.tb02171.x.

Claims

Attorney Docket No. 10034-429W012025-226 What is claimed:

1. A system comprising:one or more electrodes configured to attach to a skin region of a person, the one or more electrodes being configured to measure surface respiratory electrical myography (sEMG), surface respiratory mechanomyography (sRMG), electrocardiography (ECG), and bioimpedance across the skin region;an accelerometer circuit assembly (i.e., accelerometer PCB) configured with accelerometers, the accelerometer circuit assembly being configured to measure seismocardiography (SCG) across the skin region, wherein the accelerometer circuit assembly is positioned on the skin region;a photoplethysmographic circuit assembly (e.g., optical PCB) configured with photodiodes, the photoplethysmographic circuit assembly being configured to measure photopl ethysmography (PPG) across the skin region, wherein the a photoplethysmography circuit is positioned on the skin;a controller operatively coupled to the accelerometer circuit assembly and the photoplethysmographic circuit assembly, the controller comprising:a processor; anda memory having instructions stored thereon, wherein execution of the instructions causes the processor to:receive, via the processor, measured sEMG, sRMG, SCG, ECG, bioimpedance, and PPG signals; anddetermine, via the processor, blood volume pulsation parameter, respiratory volume surrogate parameter, respiratory phase timing parameter, and electrical and mechanical parasternal intercostal muscle activity parameter from the measured sEMG, sRMG, SCG, ECG, bioimpedance, and PPG signals,wherein the determined blood volume pulsation parameter, respiratory- volume surrogate parameter, respiratory phase timing parameter, and electrical and mechanical parasternal intercostal muscle activity parameter are outputted to provide a prolonged heart monitor for the person.

2. The system of claim 1, wherein the controller is implemented in a mobile device comprising a network interface (e.g., Bluetooth) configured to communicatively operate withAttorney Docket No. 10034-429W012025-226 the one or more electrodes, the photoplethysmographic, and the photoplethysmographic circuit assembly through a network.

3. The system of claim 1, wherein the determined blood volume pulsation parameter, respiratory volume surrogate parameter, respiratory phase timing parameter, and electrical and mechanical parasternal intercostal muscle activity parameter are subsequently employed for ambulatory care monitoring.

4. The system of claim 1, wherein the respiratory phase timing parameter is determined from an amplitude modulation operation of R-peaks in the measured SCG signal.

5. A method comprisingproviding a wearable multimodal sensing system for monitoring ventilation, cardiovascular dynamics, and respiratory muscle activity, said wearable multimodal sensing system comprising:one or more electrodes configured to attach to a skin region (e.g., sternum) of a person, the one or more electrodes being configured to measure surface respiratory electrical myography (sEMG), surface respiratory mechanomyography (sRMG), electrocardiography (ECG), and bioimpedance across the skin region;an accelerometer circuit assembly (i.e., accelerometer PCB) configured with accelerometers, the accelerometer circuit assembly being configured to measure seismocardiography (SCG) across the skin region, wherein the accelerometer circuit assembly is positioned on the skin region;a photoplethysmographic circuit assembly (e.g., optical PCB) configured with photodiodes, the photoplethysmographic circuit assembly being configured to measure photoplethysmography (PPG) across the skin region, wherein the a photoplethysmography circuit is positioned on the skin;a controller operatively coupled to the accelerometer circuit assembly and the photoplethysmographic circuit assembly, the controller comprising:a processor; anda memory having instructions stored thereon, wherein execution of the instructions causes the processor to:Attorney Docket No. 10034-429W012025-226 receive, via the processor, measured sEMG, sRMG, SCG, ECG, bioimpedance, and PPG signals; and determine, via the processor, blood volume pulsation parameter, respiratory volume surrogate parameter, respiratory phase timing parameter, and electrical and mechanical parasternal intercostal muscle activity parameter from the measured sEMG, sRMG, SCG, ECG, bioimpedance, and PPG signals,wherein the determined blood volume pulsation parameter, respiratory volume surrogate parameter, respiratory phase timing parameter, and electrical and mechanical parasternal intercostal muscle activity parameter are outputted to provide a prolonged heart monitor for the person.

6. The method of claim 5, wherein the controller is implemented in a mobile device comprising a network interface (e.g., Bluetooth) configured to communicatively operate with the one or more electrodes, the photoplethysmographic, and the photoplethysmographic circuit assembly through a network.

7. The method claim 5, wherein the determined blood volume pulsation parameter, respiratory volume surrogate parameter, respiratory phase timing parameter, and electrical and mechanical parasternal intercostal muscle activity parameter are subsequently employed for ambulatory care monitoring.

8. The method of claim 5, wherein the respiratory phase timing parameter is determined from an amplitude modulation operation of R-peaks in the measured SCG signal.

9. A device configured to be placed on a chest of a person, the device comprising: a plurality of sensors, said plurality of sensors comprising:one or more cardio-mechanical sensors;one or more optical sensors;one or more bioimpedance sensors; andone or more electrophysiology sensors including both electrocardiography and electromyography; andAttorney Docket No. 10034-429W012025-226 a processor, wherein the processor is in communication with the plurality of sensors to receive information from each of the plurality of sensors and provide hemodynamic and pulmonary mechanics information at the same time, and examine cardiopulmonary coupling characteristics.

10. The device of claim 9, wherein one or more of the plurality of sensors are placed explicitly at the intercostal muscles to capture a combination of overall pulmonary physiology (tidal volume) and local physiology (intercostal muscle activity) simultaneously.

11. The device of claim 9 or claim 1, wherein the device is configured to include both electromyogram and mechanomyogram sensing together to understand an excitation contraction coupling characteristics of intercostal muscles to evaluate fatigue.

12. The device of any one of claims 9-11, wherein the device includes a calibration step at first with a spirometer such that the received information involves relative changes in pulmonary mechanics on a personalized basis.

13. The device of any one of claims 9-12, wherein the device is attached to the chest of the person via ECG electrodes, and one or more of the plurality of sensors (e.g., PPG sensors) are then positioned against the person’s skin with a right backing force required based on the designed stiffness of the device hardware and ensuring that the plurality of sensors are proud of an overall surface of the device.

14. The device of any one of claims 9-13, wherein the device can switch modes based on a push button to go from an idle state where battery life is conserved to a full sensing state where all signals are captured.

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