Nanocomposite multimodal sensor array integrated with auxetic structure for an intelligent biometrics system

Abstract

A nanocomposite proximity-pressure sensor (NPPS), configured to measure one or more human biometrics, the NPPS including a carbon nanotube-paper (CPC) electrode, a multiwalled carbon nanotubes (MWCNTs) foam, and an auxetic MWCNTs-embedded frame, wherein the auxetic MWCNTs-embedded frame is positioned around the MWCNTs foam, wherein when the auxetic frame is compressed, it expands and exerts a pressure onto the MWCNTs foam.

Description

NANOCOMPOSITE MULTIMODAL SENSOR ARRAY INTEGRATED WITHAUXETIC STRUCTURE FOR AN INTELLIGENT BIOMETRICS SYSTEMCROSS REFERENCE TO RELATED APPLICATION

[0001] This application claims the benefit of U.S. Provisional Application 63 / 651645 filed May 24, 2024, the entire disclosure of which is hereby incorporated by reference.BACKGROUND

[0002] A multimodal sensor array, combining pressure and proximity sensing, has been of considerable interest due to the importance of ubiquitous biometrics monitoring of cardiopulmonary health and sleep behavior. However, the sensitivity and dynamic range of prevalent sensors are often insufficient for detecting subtle body changes.

[0003] Pressure and proximity sensors are of significant interest for humanmachine interfaces (HMI), unobtrusive healthcare, and sleep monitoring. Pressure sensors for biometrics monitoring still lack sensitivity and complete unobtrusiveness, especially for cardiopulmonary and sleep applications. Proximity sensors offering non-contact monitoring are costly, rigid, and prone to environmental interference, addressing practical challenges. Despite the rapid development of pressure and proximity sensors, sensors are still required to enhance sensitivity and flexibility for non-contact measurements. For example, ballistocardiography (BCG) measures the body motion in response to cardiac ejection of blood, enabling continuous heart rate and potentially blood pressure (BP) monitoring without clinical constraints. Owing to the small magnitude of BCG, the low- sensitivity pressure sensors embedded in mattresses and cushions require sophisticated signal processing, often resulting in signal loss.

[0004] Highly sensitive proximity-pressure (multimodal) sensors may be suitable for addressing the challenges by incorporating more variables in a single sensor. For instance, a porous sensor was developed as an artificial skin. The combined effect of foam deformation and fringing field disruption enabled the pressure and proximity sensing. A textile sensor was developed for monitoring pressure, proximity, and temperature during human activities. The permittivity changes with fringing field alteration enabled the pressure and proximity sensing. For HMI application, another porous structure was designed to enhance the sensitivity and expand the dynamic range. However, the previous multimodal sensors exhibited either insufficient sensitivity (<1 kPa'1) within thephysiological pressure range of humans lying on a bed (1~10 kPa), incompatible stiffness, or complicated fabrication, which hindered the actual applications. Ultimately, detecting pressure and proximity together in healthcare applications enables contextually aware and unobtrusive monitoring, facilitating early detection of health issues and enhancing overall healthcare outcomes.

[0005] Nanocomposite porous substrates have been developed as an electrode or a pressure-sensitive medium. Leveraging their tunable properties based on percolation theory, the sensitivity could be manipulated. As an electrode, the high-intensity electric field along the nanostructured substrate increases the capacitance of the electrode, consequently augmenting its response, surpassing the bulk electrode. As a pressuresensitive medium, the percolation alteration leads to significant changes in conductivity or permittivity, making the nanocomposite highly responsive to pressure. Nevertheless, nanocomposites generally exhibit a trade-off between conductivity and stiffness. The increased percolation of nanomaterials enhances conductivity and sensitivity but also results in greater stiffness, making it less compatible with the flexible human body.

[0006] Accordingly multimodal sensor arrays for detecting human biometrics are needed.SUMMARY

[0007] This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This summary is not intended to identify key features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter.

[0008] Disclosed herein is a novel capacitive nanocomposite proximity-pressure sensor (NPPS) for detecting human biometrics. NPPS consists of a carbon nanotube-paper (CPC) electrode and a percolating multiwalled carbon nanotubes (MWCNTs) foam enclosed in a MWCNTs-embedded auxetic frame. The fractured fibers in the CPC electrode intensify the electric field, enabling highly sensitive detection of proximity and pressure. When pressure is applied to the sensor, the synergic effect of MWCNTs foam and auxetic deformation amplifies the sensitivity (2.0 kPa'1). The simple and mass-producible fabrication protocol allows for building an array of highly sensitive sensors to monitor human presence, sleep posture, and vital signs, including ballistocardiography (BCG). With the aid of the machine learning algorithm, the sensor array detects blood pressurewithout intervention. This advancement allows for unrestricted vital sign monitoring during sleep or driving.

[0009] In one aspect, disclosed herein is a nanocomposite proximity-pressure sensor (NPPS), configured to measure one or more human biometrics, the NPPS comprising an electrode, a conductive foam, and an auxetic MWCNTs-embedded frame, where the auxetic MWCNTs-embedded frame is positioned around the conductive foam, where when the auxetic frame is compressed, it squeezes and exerts a pressure onto the conductive foam.

[0010] In some embodiments, the conductive foam comprises polyurethane (PU) or other porous polymer. In some embodiments, the electrode is a carbon nanotube-paper (CPC) electrode or a metal electrode. In some embodiments, the conductive foam is a multiwalled carbon nanotubes (MWCNTs) foam or another electrically conductive composite porous foam.

[0011] In some embodiments, the auxetic MWCNTs-embedded frame includes a first ring, a second ring, where the conductive foam is disposed between the first ring and the second ring, and a plurality of arms connecting the first ring and the second ring. In some embodiments, the plurality of arms is configured to bend inward in response to pressure. In some embodiments, the plurality of arms is angled between about 10 degrees and 90 degrees. In some embodiments, the plurality of arms is angled between about 30 degrees and 90 degrees. In some embodiments, the plurality of arms is angled between about 50 degrees.

[0012] In some embodiments, the human biometrics are selected from respiratory rate, heart rate, sleep posture, sleep pattern, blood pressure, lung volume, heart rate variability, respiratory efforts, proximity, phase difference between abdomen and thorax, thoracic and abdominal movement, thorax-to-abdomen breathing ratio, arterial vessel induced vibration, snoring, body position, ballistocardiography (BCG), sitting posture, or a combination thereof.

[0013] In another aspect, disclosed herein is a sensor array including a plurality of proximity-pressure sensor (NPPS), configured to measure one or more human biometrics, each NPPS including a carbon nanotube-paper (CPC) electrode, a multiwalled carbon nanotubes (MWCNTs) foam, and an auxetic MWCNTs-embedded frame, wherein the auxetic MWCNTs-embedded frame encloses the MWCNTs foam.

[0014] In some embodiments, the plurality of NPPS is four NPPS. In some embodiments, the sensor array is embedded into a mattress. In some embodiments, the sensor array is embedded into a car seat, a wheelchair, a yoga mat, an exercise mat, or a gym floor. In some embodiments, the sensor array is configured to measure heart rate, respiratory rate, sleep posture, sleep pattern, blood pressure, respiratory biometrics, proximity, ballistocardiography (BCG), sitting posture, or a combination thereof.

[0015] In another aspect, disclosed herein blood pressure measurement device including the sensor array disclosed herein, where the sensor array is a first sensor array configured to measure a chest ballistocardiography (BCG) signal, and a second sensor array configured to measure a leg BCG signal.

[0016] In some embodiments, the blood pressure measurement device further includes a blood pressure cuff. In some embodiments, the blood pressure measurement device further includes a strain sensor belt.

[0017] In yet another aspect, disclosed herein is a method of measuring blood pressure with the blood pressure measurement device disclosed herein, the method including capturing the chest ballistocardiography (BCG) signal of a patient, capturing the leg BCG signal of the patient, extracting j -peaks from the chest BCG signal and the leg signal, determining a pulse transit time (PTT), and determining blood pressure with a machine learning algorithm based on the PPT.

[0018] In some embodiments, the method further includes reducing noise of the leg BCG signal, the chest BCG signal, or both the leg BCG signal and the chest BCG signal by using signal processing methods, wherein the signal processing methods include wavelet decomposition, moving average, Savitzky-Golay filtering, or a combination thereof. In some embodiments, the machine learning algorithm is a Support Vector Machine (SVM) model.

[0019] In yet another aspect, disclosed herein is a blood pressure measurement device including the sensor array disclosed herein, where the sensor array is a first sensor array configured to measure a chest ballistocardiography (BCG) signal, and a second sensor array configured to measure an arm, a head, a finger, or a foot BCG signal.

[0020] In yet another aspect, disclosed herein is a sleep sensor mattress, including a mattress, a plurality of sensor arrays embedded into the mattress, wherein each sensor array includes four nanocomposite proximity-pressure sensor (NPPS), configured to measure one or more human biometrics, each NPPS including a carbon nanotube-paper(CPC) electrode, a multiwalled carbon nanotubes (MWCNTs) foam, and an auxetic MWCNTs-embedded frame, wherein the auxetic MWCNTs-embedded frame encloses the MWCNTs foam, and a sensor communication module.

[0021] In some embodiments, the sleep sensor mattress is configured to measure human presence, sleep posture, one or more ballistocardiography (BCG) signals, heart rate, respiratory rate, blood pressure, or a combination thereof.

[0022] In another aspect, disclosed herein is a sleep sensor mattress including four nanocomposite proximity-pressure sensor (NPPS), configured to measure one or more human biometrics, each NPPS including a carbon nanotube-paper (CPC) electrode, a multiwalled carbon nanotubes (MWCNTs) foam, and an auxetic MWCNTs-embedded frame, wherein the auxetic MWCNTs-embedded frame encloses the MWCNTs foam, a microcontroller communicatively coupled to the four NPPS, fourteen sensor arrays, wherein each sensor array comprises four NPPS, and an I2C connection communicatively coupled to the fourteen sensor arrays.

[0023] In yet another aspect, disclosed herein is a car seat device including a car seat, and the sensor array disclosed herein, wherein the sensor array is embedded into a backrest of the car seat, wherein car seat device is configured to measure a respiratory effort, a heart rate, a ballistocardiography (BCG), or a combination thereof while a vehicle is moving.DESCRIPTION OF THE DRAWINGS

[0024] The foregoing aspects and many of the advantages of this technology will become more readily appreciated as the same become better understood by reference to the following detailed description, when taken in conjunction with the accompanying drawings, wherein:

[0025] FIGURE 1 A is an example system including one or more nanocomposite proximity-pressure sensor (NPPS), in accordance with the present technology;

[0026] FIGURE IB is another example system including one or more NPPS, in accordance with the present technology;

[0027] FIGURE 1C is a closeup of FIG. IB of an NPPS, in accordance with the present technology;

[0028] FIGURES 1D-1E shows an example NPPS in pressure mode and proximity mode, respectively, in accordance with the present technology;

[0029] FIGURES 2A-2E show example NPPS fabrication methods, in accordance with the present technology;

[0030] FIGURES 3A-3C show SEM images of the pores and protruded fibers in the MWCNTs foam and CPC electrode, respectively, in accordance with the present technology;

[0031] FIGURE 4 shows an example proximity sensing mechanism and the equivalent circuit model of an NPPS, in accordance with the present technology;

[0032] FIGURES 5 A-5D show simulations of the electric field of the NPPS with and without the presence of an object (plastic, metal, or hand), in accordance with the present technology;

[0033] FIGURE 6 is a graph of a capacitance responses of an approaching plastic plate, metal plate and hand, respectively, in accordance with the present technology;

[0034] FIGURES 7A-7B are graphs showing proximity range and signal to noise ratio (SNR) of a carbon nanotube-paper composite (CPC) electrode and a copper electrode, with and without foam / auxetic frame medium, in accordance with the present technology;

[0035] FIGURES 7C-7D are graphs showing the capacitance response of five different subjects placing their hands at distances of 2, 5, and 10 cm from an NPPS, and capacitance responses of different body parts: hand, arm, and sole at a distance of 0.5 cm, respectively, in accordance with the present technology;

[0036] FIGURES 8A-8C show capacitance mapping of a hand by an NPPS array, in accordance with the present technology;

[0037] FIGURES 9A-9C show the pressure sensing mechanism and the equivalent circuit model of the NPPS and the porosity ((|>) of a MWCNTs foam and an NPPS changed as a result of compression, respectively, in accordance with the present technology;

[0038] FIGURES 9D-9F show auxetic frames having arms disposed at different angles and the resulting compressive strain, in accordance with the present technology;

[0039] FIGURE 10A shows analytical models that consider the piezocapacitance (C) and those incorporating a hybrid response (C & RCNT) with the experimental capacitance changes due to compression, in accordance with the present technology;

[0040] FIGURE 10B shows simulated mechanical properties of the NPPS with varying angle (9) of the diagonal auxetic structure, in accordance with the present technology;

[0041] FIGURE 10C shows capacitance responses of the applied pressure from the human hand, a plastic plate, and a metal plate, in accordance with the present technology;

[0042] FIGURE 10D shows frequency-dependent capacitance responses, in accordance with the present technology;

[0043] FIGURE 10E shows comparisons between the capacitance response (NPPS) and displacement (double-integral of acceleration) under a 2 Hz sine wave motion, in accordance with the present technology;

[0044] FIGURE 10F shows a fatigue test under 8000 loading / unloading cycles, in accordance with the present technology;

[0045] FIGURE 11A shows real-time capacitance response of a human approached, contacted, and pressed on an NPPS, in accordance with the present technology;

[0046] FIGURE 1 IB shows capacitance responses of a plastic plate, a metal plate, a glass beaker filled with water, and the human hand at proximity and pressure sensing modes, in accordance with the present technology;

[0047] FIGURES 12A-12C show detection of BCG signal under chest along with an accelerometer as a control, in accordance with the present technology;

[0048] FIGURE 12D show detection of respiratory effort from an NPPS array along with a strain sensor aided in belt, in accordance with the present technology;

[0049] FIGURE 13 A shows detection of respiratory effort and BCG from a car seat embedded with an NPPS array in a moving car (~25 km / hr), in accordance with the present technology;

[0050] FIGURE 13B shows a comparison of the capacitance responses of a backpack, a plastic box filled with metal blocks (0.9 kPa), and a human pressing an NPPS array embedded on the backrest, in accordance with the present technology;

[0051] FIGURES 14A-14D are schematic illustrations of a mattress embedded with 56 NPPSs, communication method, FCN sleep posture prediction model, and the dataset generated from the mattress, and accurate prediction of a posture (Foetus, facing left) by the FCN sleep posture prediction model, respectively, in accordance with the present technology;

[0052] FIGURES 15A-15C are schematic illustrations of the setup and machine learning-based method for BP estimation, detection of leg BCG signal along with anaccelerometer as a control, where the signal was undetectable by the commercial accelerometer due to its low magnitude, in accordance with the present technology;

[0053] FIGURES 16A-16B show dynamic BP values from two subjects under BP perturbing interventions, in accordance with the present technology;

[0054] FIGURES 16C-16F show a correlation and Bland- Altman plots between estimated SBP / DBP using the NPPS array system and reference SBP / DBP using cuff-based device, in accordance with the present technology;

[0055] FIGURES 17A-17B show Continuous BP estimation using the NPPS array system, commercial polysomnography (PSG), and reference cuff device (* : 30-sec breath hold), in accordance with the present technology; and

[0056] FIGURE 18 is a wearable NPPS, in accordance with the present technology.DETAILED DESCRIPTION

[0057] Disclosed herein are example nanocomposite proximity pressure sensors (NPPS). The NPPS may include a carbon nanotube-paper composite (CPC) electrode, a multiwalled carbon nanotubes (MWCNTs)-coated foam, and a MWCNTs-embedded auxetic frame. The MWCNTs foam enclosed in the MWCNTs auxetic frame may be added to the CPC electrode. The proximity and pressure responses of the NPPS are enhanced by the disruption of high-intensity electric field and the permittivity changes augmented by auxetic deformation. The high aspect ratio fibers of the CPC electrode may enhance the electric field, and thus the proximity and pressure sensitivity. Additionally, the synergic effect of the MWCNTs foam and auxetic deformation may amplify the sensitivity to external pressure. In one aspect, disclosed herein is an array of NPPSs embedded in a mattress or a foam pad to detect human presence, discern sleep posture, capture ballistocardiography (BCG) signals, and monitor vital sign including blood pressure, heart rate, and respiratory rate. This NPPS and the NPPS systems disclosed herein are configured for unconstrained vital sign monitoring both in sleep and driving. In some embodiments, the NPPS system may include a wearable NPPS for biometric monitoring.

[0058] FIG. 1 A is an example system 1000 including one or more nanocomposite proximity-pressure sensor (NPPS) 100, in accordance with the present technology. In some embodiments, the system 1000 includes a foam pad (or mattress) 1010. One or more NPPS 100A, 100B, 100C... 100N are embedded in the foam pad 1010. In operation, when aperson or patient lays on the foam pad 1010, the one or more NPPS 100A, 100B, 100C. . . 100N may measure a number of health metrics (or “human biometrics”) as described in detail herein.

[0059] FIG. IB is another example system 1000 including one or more NPPS100, in accordance with the present technology. In some embodiments, the one or more NPPS 100 are arranged in an array in the mattress 1010.

[0060] FIG. 1C is a closeup of 1C of FIG. IB of an NPPS 100, in accordance with the present technology. In some embodiments, the NPPS 100 includes an electrode101. In some embodiments, the electrode 101 is a CPC electrode. In some embodiments, the electrode is a metal electrode. In some embodiments, the NPPS 100 includes a foam 103. In some embodiments, the foam 103 is a conductive foam. In other embodiments, the foam 103 is multi-walled carbon nanotubes (MWCNTs) foam. In some embodiments, the NPPS 100 includes an auxetic frame 105. The auxetic frame 105 may include a first ring 111A, a second ring 11 IB, and one or more arms 107A, 107B, 107C... 107N (collectively a “plurality of arms”). In some embodiments, the MWCNTs foam 103 is disposed between the first ring 111 A and the second ring 11 IB. In some embodiments, the plurality of arms 107A, 107B, 107C... 107N connect the first ring 111A and the second ring 11 IB. In some embodiments, the plurality of arms 107A, 107B, 107C... 107N is configured to bend in response to pressure, as described in detail herein.

[0061] In some embodiments, the plurality of arms 107A, 107B, 107C... 107N is angled between about 10 degrees and 90 degrees. In some embodiments, the plurality of arms 107A, 107B, 107C... 107N is angled between about 30 degrees and 90 degrees. In some embodiments, the plurality of arms 107A, 107B, 107C... 107N is angled between about 50 degrees.

[0062] As shown in FIGs. 1 A-1B, the NPPS 100 may be a standalone device, or incorporated into a system, such as embedded into the foam pad 1010.

[0063] In operation, the system 1000 is configured to measure one or more himan biometrics. In some embodiments, the human biometrics are selected from respiratory rate, heart rate, sleep posture, sleep pattern, blood pressure, lung volume, heart rate variability, respiratory efforts, proximity, phase difference between abdomen and thorax, thoracic and abdominal movement, thorax-to-abdomen breathing ratio, arterial vessel induced vibration, snoring, body position, ballistocardiography (BCG), sitting posture, or a combination thereof.

[0064] FIGs. 1D-1E shows an example NPPS 100 in pressure mode (FIG. IE) and proximity mode (FIG. ID), respectively, in accordance with the present technology.

[0065] In some embodiments, a multimodal sensor array (such as array 100 A, 100B, 100C. . . 100N in FIG. 1 A) detects body motion through both proximity and pressure sensing modes. The proximity sensing mode, as shown in FIG. ID, monitors the body movement without physical contact.

[0066] In the pressure sensing mode, the sensor array detects subtle pressure changes induced by body motion under the significant influence of body weight, expressed as:

[0067] Here Po and Pi denote the pressures from the body weight and motion, coi is the radian frequency of / th wave, t is time, and i is integers (0, 1, 2, . . .). The amplitude of Po (1—10 kPa) can be ten thousand times that of Pi (1-1000 Pa), posing a challenge for sensor development. The NPPS 100 design is configured to address the loading conditions given by a significant preload (Po) and subtle pressure changes (Pi).

[0068] To increase both proximity range and pressure sensitivity, a NPPS 100 utilizing self-capacitance was designed. In some embodiments, the NPPS consists of one single capacitive electrode made of carbon nanotube paper composite (CPC) and one multiwalled carbon nanotubes (MWCNTs) foam enclosed in one MWCNTs-embedded auxetic frame. In such embodiments, the high aspect ratio fibers surrounding the CPC electrode projected a high-density electric field along the edges of the fibers, enabling sensitive proximity and pressure detection. The sensitivity to pressure was further amplified through the synergistic effect of the MWCNTs foam and auxetic deformation. MWCNTs were chosen for substrate coating due to their high electrical conductivity and versatility of coating. MWCNTs of 0.4 mg / mL were selected to coat the foam and auxetic frame according to the percolation analysis. The dielectric constants (a) of the MWCNTs foams at different MWCNTs concentrations followed the power law in the percolation theory:q

[0069] where Pc is the upper threshold of percolation, PCNT is the concentration of MWCNTs coated on foam, and S is the critical exponent. In some embodiments, the foam is polyurethane (PU). At 0.4 mg / mL, MWCNTs foam exhibited a large response to compression and a broader dynamic range which conformed with loading conditions with a preload.

[0070] The stiffness is reduced when the arms of the auxetic structure are angled at 50 degrees. In some embodiments, the arms of the auxetic structure are angled at about50 to about 90 degrees (as shown in FIGs. 9D-9F). When organized in an array, it is possible to map and visualize proximity and geometry with the array.EXAMPLES

[0071] FIGS. 2A-2E show example NPPS 100 fabrication methods, in accordance with the present technology. A scaled-up manufacturing process was developed to fabricate the array of NPPSs (as shown in FIG. 2E). In some embodiments, the CPC electrode 101 (11 mm in diameter, 0.1 mm in thickness) is prepared using a wetfracture method. In one example, to produce a large number of fractured fibers surrounding the CPC electrode, the CPC electrode may be designed as a circular shape. First, cellulosic paper is coated with MWCNTs by applying an aqueous solution with 5 mg / mL-MWCNTs dispersed in a surfactant (sodium dodecyl sulfate; SDS; 1%). For electrical connection, silver paste is deposited on the MWCNTs-coated paper through screen printing (silver patterning). After stamping water drops on the MWCNTs-coated paper, a circular punch applies a tensional fracture to the paper to form the CPC electrode. A 100 pm gap between the punch and the hole enables the tensional fracture around the circular edge. Finally, the punched CPC electrode is protected by a thin polyimide film. In fracture, the high aspect ratio fibers are individualized and protruded along the stretching direction (as shown in FIGs. 3A-3C). In one example, the linear density, average length, and average thickness of the fibers were 31.8 ± 5.3 mm-1, 1.4 ± 0.3 mm, and 5.9 ± 1.7 pm, respectively. The aspect ratio was as high as 244 (length: thickness). The sheet resistance of the CPC electrode was 296.8 ± 32.3 Q / sq, measured via a four-point probe.

[0072] In one example, the MWCNTs foam 103 was fabricated by dip-coating a polyurethane (PU) foam (20 mm in diameter, 14 mm in height) in 0.4 mg / mL MWCNTs solution (1% SDS, FIG. 2B). A 3D-printed thermoplastic polyurethane (TPU) auxetic frame 105 (30 mm in diameter, 14 mm in height) was also coated with MWCNTs using ultrasonic cavitation treatment in 0.4 mg / mL MWCNTs aqueous SDS solution. MWCNTs were nonspecifically coated on both polyurethane (PU) and TPU matrices. Finally, the MWCNTs foam 103 was enclosed in the MWCNTs-auxetic frame 105 and was stacked on the CPC electrode 101 to form the NPPS 100.

[0073] FIG. 2E shows an example of a scale-up manufacturing process. In one example, a punching array was developed, capable of simultaneously fracturing a 2x10 punch set by a steel block. With the even fracture applied on the uniformly wet MWCNTs paper, the scale-up punching method yielded a success rate exceeding 80%. An array ofNPPSs could be easily fabricated through the scale up process and embedded in a foam pad (or mattress, such as mattress 1010) to form an intelligent biometric system.

[0074] FIGs. 3A-3C show SEM images of the pores and protruded fibers in the MWCNTs foam (FIG. 3B) and CPC electrode (FIG. 3C), respectively, in accordance with the present technology. In some embodiments, the NPPS employs a single-electrode capacitive system to enhance proximity range and discern the human body from the environment. The CPC electrode projected a high-intensity electric field through a MWCNTs foam and an auxetic frame, resulting in a substantial capacitance response.

[0075] FIG. 4 shows an example proximity sensing mechanism and the equivalent circuit model of an NPPS, in accordance with the present technology. The proximity sensing model may include two serially connected capacitors. As described herein, the MWCNTs foam includes a foam medium (such as PU) coated in MWCNTs. Csdenotes the capacitance of the NPPS, while Cj represents the capacitance between the NPPS and the object. When an object approached the NPPS, the disrupted degree of the electric field induced the capacitance change.

[0076] FIGs. 5A-5D show simulations of the electric field of the NPPS with and without the presence of an object (plastic, metal, or hand), in accordance with the present technology. The electric field of the NPPS with and without the presence of an object was simulated using COMSOL. A grounded object (human) demonstrated a strong disruption to the electric field, indicating that humans would induce the highest capacitance response among objects at the same proximity. The human body contains an enormous number of charges, allowing it to absorb an electric field as a virtual ground. The plastic and metal objects, being ungrounded, caused limited disruption to the electric field, resulting in smaller capacitance responses.

[0077] FIG. 6 is a graph of capacitance responses of an approaching plastic plate, metal plate and hand, respectively, in accordance with the present technology. On the vertical axis is the change in capacitance AC in picofarads. On the horizontal axis is distance in centimeters. To characterize the proximity sensing capabilities of the NPPS, proximity tests were conducted. Due to the high-intensity electric field from the CPC electrode and the large surface area of the MWCNTs foam / auxetic frame medium, the detectable human proximity range of the NPPS was extended to 35 cm, where the signal could be distinguished from the noise. The proximity range (35 cm) was much larger than the previous multimodal sensors (<20 cm). Furthermore, the capacitance response tohuman proximity was significantly higher than that of the other objects due to its strong distribution to the electric field.

[0078] FIGs. 7A-7B are graphs showing proximity range and signal to noise ratio (SNR) of a carbon nanotube-paper composite (CPC) electrode and a copper electrode, with and without foam / auxetic frame medium, in accordance with the present technology. The NPPS exhibited an extended proximity range (FIG. 7A) and a high signal-to-noise ratio (SNR) (FIG. 7B). At a human proximity of 21 cm, SNR of an NPPS was 3.9 and 6.9 times that of a copper electrode with and without a MWCNTs foam / auxetic frame medium.

[0079] FIGs. 7C-7D are graphs showing the capacitance response of five different subjects placing their hands at distances of 2, 5, and 10 cm from an NPPS, and capacitance responses of different body parts: a hand, an arm, and a sole of a foot at a distance of 0.5 cm, respectively, in accordance with the present technology. The capacitance response was tested among human subjects (N=5). Each subject placed the hand above an NPPS at various distances (2, 5, and 10 cm). The small response errors of 6.5 % resulted from the saturation of charge numbers in the human body. The charges in the human body were substantial, rendering the charge difference among the subjects and the body parts (e.g., hand, arm, and sole) negligible.

[0080] FIGs. 8A-8C show capacitance mapping of a hand by an NPPS array, in accordance with the present technology. Furthermore, an array of NPPSs may be configured to map the proximity and geometry of a hand without physical contact.

[0081] FIGs. 9A-9C show the pressure sensing mechanism and the equivalent circuit model of the NPPS and the porosity ((|>) of a MWCNTs foam and an NPPS changed as a result of compression, respectively, in accordance with the present technology. The synergetic effect of the MWCNTs foam and auxetic deformation on the high SNR CPC electrode augmented the capacitance response to pressure. When vertical pressure was applied, the MWCNTs auxetic frame squeezed the MWCNTs foam in horizontal direction. The porosity ((|Φ) of the MWCNTs foam reduced to enhance the permittivity and conductivity, thereby increasing capacitance. TheΦ (|) of the MWCNTs foam and the NPPS under compression are shown. The (|) of the MWCNTs foam was 1.4 times greater than that of the NPPS when compressive strain (ec) was greater than 0.5, indicating the further reduction in (|) due to the auxetic frame. The equivalent circuit for the NPPS is the serial connection of a capacitor of CPC electrode (Ce) to a porous medium consisting of a parallel connection of air (Cair) and MWCNTs (CCNT) capacitors and a piezoresistor (RCNT) Qiirand CCNT denote the capacitances of air and MWCNTs volume fraction in the medium, which are expressed as:Equation 4

[0082] where 80is the vacuum permittivity (8.85 x 10-12 F / m), is thedielectric constant of air (8;ur=1), isthe dielectric constant of MWCNTs in the foam,A is the area of the NPPSand t is the thickness of thevalues were obtained from experimental measurements. The total impedance (Z) of the NPPS is:

[0083] The pressure-induced change in capacitance (C) is derived from the imaginary part (X) of the Z as:Equation 6

[0084] The model incorporating both piezocapacitance and provided amore accurate description of the NPPS compared to the model that solely considered piezocapacitance.

[0085] FIGs. 9D-9F show auxetic frames having arms disposed at different angles (50° and 90°, respectively) and the resulting compressive strain (FIG. 9F), in accordance with the present technology.

[0086] FIG. 10A shows analytical models that consider the piezocapacitance (C) and those incorporating a hybrid response (C & RCNT) with the experimental capacitance changes due to compression, in accordance with the present technology. Under large compression R<’NT significantly decreased due to the physical contacts ofMWCNTs, resulting in a higher capacitance change. Moreover, the NPPS exhibited 2.7times capacitance changes than that of the 0.4 mg / mL MWCNTs foam. This emphasized the crucial role of the auxetic frame in reducing porosity, thereby leading to a high capacitance response.

[0087] FIG. 10B shows simulated mechanical properties of the NPPS with varying angle) of the diagonal auxetic structure, in accordance with the present technology. The diagonal auxetic frame with an angle ) could enhance the sensitivitywithout increasing the structural stiffness. The auxetic structure generally yielded higher stiffness by redistributing stress, rendering it incompatible with soft mattresses or cushions. An auxetic frame with 50° angle comprising longer auxetic ribs resulted in larger deflection per Euler-Bernoulli theory, thereby reducing the stiffness. Furthermore, the partial vertical force was converted to torque, exhibiting an 8° of twisting, which also contributed to the reduced stiffness. When ecwas between 9.3 and 9.8, the stiffness of the NPPS was comparable to that of the MWCNTs foam, while initially the stiffness of theNPPS (E=3.1 kPa) was two times the MWCNTs foam

[0088] FIG. 10C shows capacitance responses of the applied pressure from the human hand, a plastic plate, and a metal plate, in accordance with the present technology.

[0089] To characterize the pressure sensing capabilities of the NPPS, pressure tests were conducted. Pressure sensitivity (S) is: J0

[0090] where Codenotes the initial capacitance, and AP denotes the applied pressure. The capacitance response of the NPPS exhibited three distinct sensitivities: 0.20, 2.00, and 0.27 kPa-1 in the low-pressure region (0~l kPa), pressure-sensitive region (1~3 kPa), and high-pressure region (3—14 kPa), respectively. The NPPS spanned the physiological pressure range of the human body (1—10 kPa). Assessing the detection limit (DL) of the NPPS was challenging, as its sensitivity extended to pressure and object’s permittivity. Placing a small weight on an NPPS could result in biased capacitance influenced by both pressure and permittivity. Therefore, DL was computed by DL = where a denotes the standard deviation of background signals (<r=0.0003), Sdenotes sensitivity (S=2.00 kPa-1), and SNR denotes desired signal-to-noise ratio (SNR=2). The DL was 0.3 Pa in the pressure-sensitive region. Moreover, only the pressure applied by humans showed a significant response (12 times that by plastic and metalobjects), as the human body acted as a virtual ground, terminating the electric field, similar to the proximity sensing mode (as shown in FIG. ID). The result indicated the capability of the NPPS to recognize humans and further engage in the detection of subtle body movement (e.g., BCG) on the mattress. The sensing performance of the NPPS (N = 6) was highly reproducible, showing an error below 4.8%.

[0091] FIG. 10D shows frequency-dependent capacitance responses, in accordance with the present technology. The frequency-dependent tests in the clinically important frequency range (1~8 Hz) showed a consistent and quantitative response.

[0092] FIG. 10E shows comparisons between the capacitance response (NPPS) and displacement (double-integral of acceleration) under a 2 Hz sine wave motion, in accordance with the present technology. A 2 Hz sine wave pressed an NPPS under 1 to 1.5 kPa alongside an accelerometer (ISM330DHCX, SparkFun) as a control. The displacement was calculated through the double integral of the acceleration. The NPPS exhibited a 24 ms delay time due to the viscoelastic creep of the medium. This recovery time allowed the NPPS to transduce the motion up to 42 Hz (1 / 24 ms), spanning the frequency range of BCG (l~10 Hz).

[0093] FIG. 10F shows a fatigue test under 8000 loading / unloading cycles, in accordance with the present technology. The 8000 cyclic pressure tests under 1~4 kPa showed a reliable capacitance response.

[0094] FIG. 11A shows real-time capacitance response of a human approached, contacted, and pressed on an NPPS, in accordance with the present technology. The realtime capacitance response was demonstrated as a human approached, contacted, and pressed on an NPPS.

[0095] FIG. 1 IB shows capacitance responses of a plastic plate, a metal plate, a glass beaker filled with water, and the human hand at proximity and pressure sensing modes, in accordance with the present technology. In addition, the NPPS exhibited the capability of recognizing humans in both proximity and pressure sensing modes. Humans induced the highest response in both modes. In comparison to other previous results, the NPPS demonstrated the highest combined performance, incorporating a pressure sensitivity of 2.00 kPa-1 and a proximity range of 35 cm. The NPPS low stiffness and simple fabrication enables various applications through utilizing an NPPS array.

[0096] FIGs. 12A-12C show detection of BCG signal under chest along with an accelerometer as a control, in accordance with the present technology. The high sensitivityof the NPPS enables the detection of recognizable I, J, and K waves of BCG from the human body. An NPPS array consisting of four NPPSs was embedded in a mattress positioned under the chest for the purpose of capturing BCG, while an accelerometer was attached on the chest as a control. The NPPS array and accelerometer measured BCG without direct skin contact (cloth between sensors and body). Wavelet decomposition (sym4, level 2) and Savitzky-Golay filtering in MATLAB were applied to denoise the signal, yielding a clear BCG waveform. The NPPS array detected BCG, while the signal from the accelerometer exhibited a different morphology but same peak timing due to the body compliance (accelerometer positioned on the chest; NPPS array positioned beneath the chest). The heart rate (HR) was measured simultaneously using the gold standard ECG and the NPPS array under the chest. NPPS array demonstrated a low mean absolute error (MAE) of 0.8 BPM with a low standard deviation of absolute error (SDAE) of 0.7 BPM with respect to the ECG. A high correlation of HR existed between the ECG and the NPPS array (r = 0.94).

[0097] FIG. 12D show detection of respiratory effort from an NPPS array along with a strain sensor aided in belt, in accordance with the present technology. The NPPS array enables the monitoring of various respiratory biometrics. The thoracic respiratory effort was measured by the NPPS array (four NPPSs) in a mattress along with a strain sensor aided in belt (SOMNOTOUCH) as the standard. Three different respiratory patterns (normal, rapid, and deep breathing) were simulated and successfully detected by the NPPS array in a high agreement to the standard. Furthermore, the tidal volume can potentially be estimated by analyzing the morphology of respiratory effort. A high correlation coefficient of 0.94 between the NPPS array and a standard spirometer was demonstrated.

[0098] FIG. 13 A shows detection of respiratory effort and BCG from a car seat embedded with an NPPS array in a moving car (~25 km / hr), in accordance with the present technology. Moreover, an NPPS array (four NPPSs) was embedded in the backrest of a car seat, which could clearly differentiate humans from other objects (e.g., backpack and box filled with metal blocks) due to the higher capacitance response.

[0099] FIG. 13B shows a comparison of the capacitance responses of a backpack, a plastic box filled with metal blocks (0.9 kPa), and a human pressing an NPPS array embedded on the backrest, in accordance with the present technology. In addition, vital signs could even be measured in a moving car (~25 km / hr). The NPPS array successfully measured respiratory effort (0.2-0.3 Hz) and BCG (2-10 Hz) under the influence of carswaying (0.5-1 Hz). The peaks in the BCG signals exhibited a heartbeat frequency (72 BPM).

[0100] FIGs. 14A-14D are schematic illustrations of a mattress embedded with 56 NPPSs, communication method, FCN sleep posture prediction model, and the dataset generated from the mattress, and accurate prediction of a posture (Foetus, facing left) by the FCN sleep posture prediction model, respectively, in accordance with the present technology. For sleep posture recognition, a total of 56 NPPSs were embedded in a mattress. Four NPPSs were controlled by one microcontroller unit (MCU), and fourteen units were communicated through I2C connection (FIG. 4E). The capacitance mapping of a human body on the mattress is shown (FIG. 4E). A total of 9 postures were simulated (Supine, Starfish, Freefaller, and Foetus / Log / Yearner facing left or right). The capacitance mapping was converted into grayscale images for the dataset used in a fully convolutional network (FCN) model. The dataset consisted of 727 grayscale images of sleep postures, which was augmented by random rotation and zoom-in / out. The prediction accuracy reached 97.2% with only 56 sensors, compared to a system using 1728 sensors with an accuracy of 97.9% and another system using 195 sensors with an accuracy of 89.9%. The developed mattress accurately predicted a test case (Foetus, facing left). The prediction accuracy for the following postures: Foetus, Log, and Yearner was lower due to their high similarity in position. The ability of the NPPS array to detect both proximity and pressure enhanced the mapping of the human body shape, especially in the edge and suspended regions like the neck and knee. The combined use of a high-sensitivity NPPS array and FCN model showcased the promising applications of an intelligent biometric system for recognizing human presence, detecting vital signs, and predicting sleep posture.

[0101] FIGs. 15A-15C are schematic illustrations of the setup and machine learning-based method for BP estimation, detection of leg BCG signal along with an accelerometer as a control, where the signal was undetectable by the commercial accelerometer due to its low magnitude, in accordance with the present technology. Utilizing two NPPS arrays enables the detection of phase difference between the BCG signals from two arterial sites (chest and legs), aiding in the cuff-less BP estimation. This phase difference indicated pulse transit time (PTT), which was the time of a pulse traveling between two arterial sites and inversely related to BP. The NPPS array under the legs captured a signal, exhibiting peaks around the heartbeat frequency (-1 Hz). This signal stemmed from leg tissue displacement caused by the pulsation in the major leg arteries,which was termed as leg BCG in this study. Given the NPPS’s high sensitivity (2 kPa-1), it could capture leg tissue displacement in a non-cuff setting, surpassing the commercial sensors. Importantly, this signal was not transmitted from the chest BCG to legs through joints when a person was lying down, as joints were considered soft connections with high acoustic impedance. To confirm, external forces at 1 Hz and 2 Hz were applied to the torso, which was not transmitted to the legs.

[0102] The BP estimation method involved four steps (1) noise reduction of chest and leg BCG signals; (2) extraction of J-peaks; (3) calculation of PTT; (4) machine learning BP estimation. In step 1, wavelet decomposition and Savitsky-Golay filtering were applied for clear BCG waves. In step 2, envelope and peak detection functions were utilized to extract peaks. In step 3, cross-correlation function was used to compute the phase difference every 5-sec time window, determining PTT. In step 4, a Support Vector Machine (SVM) model, incorporating multiple variables related to BP, such as 1 / PTT, HR, LF HRV (power of low-frequency heartrate variability, 0.04-0.15 Hz), and I-J interval (time interval between I-wave and J-wave of chest BCG), was trained to estimate BP.

[0103] FIGs. 16A-16B show dynamic BP values from two subjects under BP perturbing interventions, in accordance with the present technology. The NPPS arrays demonstrate a high agreement in tracking both dynamic and absolute changes in BP compared to the reference device. In the experiment, BCG signals, along with reference BP, were measured 6 times per subject while they remained in the supine position. A standard oscillometric BP monitor was taken as a reference using a brachial cuff (OMRON BP5250). A series of interventions (lying still for 30 sec and breath hold for 30 sec) were performed three times to elicit BP changes. Each intervention included a 2-min resting. The data from 11 subjects, comprising a total of 66 BP data points, were recorded. The results for systolic BP (SBP) and diastolic BP (DBP) estimation in two subjects are shown. Results for the other 9 subjects also indicated promising performance.

[0104] FIGs. 16C-16F show a correlation and Bland- Altman plots between estimated SBP / DBP using the NPPS array system and reference SBP / DBP using cuff-based device, in accordance with the present technology. A high correlation existed between the reference BP and the estimated SBP (r = 0.83) and DBP (r = 0.72). The estimated SBP showed a low mean absolute error (MAE = 3.3 mmHg) and a low standard deviation of error (SD = 5.1 mmHg), while the estimated DBP exhibited a MAE = 3.8 mmHg and SD = 5.7 mmHg. Furthermore, the limit of agreement (LoA = 1 ,96*SD) was computed for bothSBP (LoA = 9.9 mmHg) and DBP (LoA = 11.1 mmHg). The comparison of BP estimation performance between the NPPS arrays and the relevant system is presented (Table 1). The NPPS arrays showed a promising performance in terms of correlation, MAE, and comparable SD compared to the relevant system. The machine learning-based BP estimation model, incorporating crucial variables input (1 / PTT, HR, LF HRV, and I-J interval), improved the accuracy compared to the nonlinear regression modal. To evaluate the performance of continuous BP monitoring, a polysomnography (PSG) device (SOMNOtouch NIBP, Somnomedics, Germany) was utilized, employing ECG and PPG to derive BP from pulse arrival time (PAT), a surrogate for PTT, for comparison.

[0105] FIGs. 17A-17B show Continuous BP estimation using the NPPS array system, commercial polysomnography (PSG), and reference cuff device (* : 30-sec breath hold), in accordance with the present technology. The BP perturbing intervention was performed to elicit BP change. The NPPS arrays demonstrated a high agreement in monitoring real-time SBP (MAE = 2.7 mmHg; SD = 2.6 mmHg) and DBP (MAE = 2.7 mmHg; SD = 1.7 mmHg) compared to the PSG.

[0106] In conclusion, a mattress embedded with 56 high-sensitivity NPPSs using machine learning methods could serve as a promising intelligent biometric system, accomplishing: (1) recognizing human presence and sleep posture; (2) capturing BCG signals from the arrays under the chest and legs; and (3) monitoring vital sign (HR and RR) and BP in a fully unobtrusive manner.

[0107] a)Pearson correlation coefficient;b)limit of agreement;c)mean absolute error;d)standard deviation of error;e) representative system for comparison.

[0108] The mean capacitance value of NPPS tracks regional blood volume changes, reflecting mean arterial pressure. By incorporating mean capacitance with pulse transit time (phase difference), the accuracy of blood pressure measurement is enhanced. Blood pressure measurements based on phase differences between chest and leg BCG tend to yield lower values and reduced BP fluctuations compared to reference methods. When a human lies on the NPPS, the system detects both physical motion (BCG and body movement) and regional blood volume changes in major arteries or the heart by sensing the distribution of the electric field within the body. The mean capacitance values from NPPS track these regional blood volume changes, similar to photoplethysmography, and reflect mean blood pressure. Combining these mean capacitance values with phase difference-based blood pressure estimation enhances the accuracy of blood pressure measurement.

[0109] This study presents a novel capacitive nanocomposite proximity-pressure sensor (NPPS) comprising a carbon nanotube-paper composite (CPC) electrode and a MWCNT-foam enclosed by a MWCNT-auxetic frame. The high aspect ratio fibers in the CPC electrode intensified the electric field, enabling highly sensitive proximity and pressure detection. The high intensity electric field and large surface area of MWCNTs porous medium extended the proximity range to 35 cm. The synergistic effect of the MWCNTs foam and auxetic deformation amplified the sensitivity to pressure (2.0 kPa-1). The auxetic frame and foam allowed the NPPS to support body weight and capture subtle body movement while remaining the stiffness similar to the original foam. Embeddingarrays of NPPS in a mattress with machine learning algorithms allowed the detection of human presence, sleep posture, BCG, and vital signs (HR, RR, and BP) without skin contact. The pulse transit time (PTT) measured by two NPPS arrays showed a high correlation to BP, demonstrating MAE = 3.3 mmHg and SD = 5.1 mmHg in comparison to cuff-based BP monitor. Fewer sensors enhanced accuracy in sleep postures prediction and BP estimation due to the multimodal sensing mechanism and high sensitivity. This advancement holds promise for unconstrained vital sign monitoring, especially during sleep.

[0110] FIGURE 18 is a wearable NPPS 100, in accordance with the present technology. In some embodiments, the NPPS 100 includes an electrode 101, such as a CPC electrode, a foam 103, and an auxetic frame 103, as disclosed herein. In some embodiments, the NPPS 100 further includes a physical constraint 300 (e.g., strip or belt) to make a wearable sensor. When the NPPS 100 is physically constrained to the skin with a belt or tape 300, it functions as if it were embedded in a mattress to detect physical motion applied to the NPPS 100. When placed on the chest, the NPPS 100 can measure chest BCG and respiratory rate.[OHl] Cellulosic paper was applied twice with 5 mg / mL MWCNTs (Cheap Tubes Inc., VT, USA) dispersed in aqueous sodium dodecyl sulfate (SDS, 1%, Sigma- Aldrich, MO, USA) (Movie SI). A wet cellulosic pulp was oven-dried at 80°C for 10 min. Silver paste (MG Chemicals, USA) was screen-printed to the center of CPC and cured (Figure S2). Drop of deionized water was applied on a CPC tailored to the fracture location. A CPC with fractured fibers was produced by punching the wet paper through a circular punch (Figure S2). A polyimide tape was attached to protect the CPC after wire connection.

[0112] Fabrication of MWCNTs foam and auxetic frame'. A 14 mm height polyurethane (PU) foam (20 mm in diameter) was cut using a hole saw drill bit. MWCNTs were deposited onto the PU foam via dip-coating with a 0.4 mg / mL MWCNTs aqueous SDS solution. The wet foam was oven-dried at 80°C for 1 hr. An auxetic frame was 3D- printed (Original Prusa i3 MK3+, Prusa Research, Czech Republic) using the thermoplastic polyurethane filament (TPU, flexible 1.75 mm, Priline Technology). MWCNTs were coated onto the TPU auxetic frame using ultrasonic cavitation treatment (Continuous mode, 1 hr, 70% power, Branson SFX550 Sonifier, USA) in a 0.4 mg / mL MWCNTs aqueous SDS solution. The wet TPU was oven-dried at 80°C for 1 hr.

[0113] Characterization of the NPPS: An NPPS was characterized through controlled pressure and proximity tests. Compression strain (pressure) and proximity were controlled by a modified 3D printer. Capacitance value was measured using a commercial chip (AD7747, Analog Devices Inc.). The force applied to the NPPS was measured with a load cell (DYMH-103 10kg, CALT sensor). For NPPS array, 4 NPPSs were controlled by one MCU, and their capacitances were measured by a commercial 4-channel chip (FDC1004, Texas Instrument).

[0114] Human subject test: The study related to sleep and vital sign monitoring (HR and RR) were approved by the institutional review board (IRB) at University of Washington (UW) (IRB ID: STUDY00018275). The study related to blood pressure monitoring was approved by IRB at UW (IRB ID: STUDY00019217).

[0115] The present application may reference quantities and numbers. Unless specifically stated, such quantities and numbers are not to be considered restrictive, but representative of the possible quantities or numbers associated with the present application. Also, in this regard, the present application may use the term “plurality” to reference a quantity or number. In this regard, the term “plurality” is meant to be any number that is more than one, for example, two, three, four, five, etc. The terms “about,” “approximately,” “near,” etc., mean plus or minus 5% of the stated value. For the purposes of the present disclosure, the phrase “at least one of A, B, and C,” for example, means (A), (B), (C), (A and B), (A and C), (B and C), or (A, B, and C), including all further possible permutations when greater than three elements are listed.

[0116] Embodiments disclosed herein may utilize circuitry in order to implement technologies and methodologies described herein, operatively connect two or more components, generate information, determine operation conditions, control an appliance, device, or method, and / or the like. Circuitry of any type can be used. In an embodiment, circuitry includes, among other things, one or more computing devices such as a processor (e.g., a microprocessor), a central processing unit (CPU), a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or the like, or any combinations thereof, and can include discrete digital or analog circuit elements or electronics, or combinations thereof.

[0117] An embodiment includes one or more data stores that, for example, store instructions or data. Non-limiting examples of one or more data stores include volatile memory (e.g., Random Access memory (RAM), Dynamic Random Access memory(DRAM), or the like), non-volatile memory (e.g., Read-Only memory (ROM), Electrically Erasable Programmable Read-Only memory (EEPROM), Compact Disc Read-Only memory (CD-ROM), or the like), persistent memory, or the like. Further non-limiting examples of one or more data stores include Erasable Programmable Read-Only memory (EPROM), flash memory, or the like. The one or more data stores can be connected to, for example, one or more computing devices by one or more instructions, data, or power buses.

[0118] In an embodiment, circuitry includes a computer-readable media drive or memory slot configured to accept signal-bearing medium (e.g., computer-readable memory media, computer-readable recording media, or the like). In an embodiment, a program for causing a system to execute any of the disclosed methods can be stored on, for example, a computer-readable recording medium (CRMM), a signal -bearing medium, or the like. Nonlimiting examples of signal-bearing media include a recordable type medium such as any form of flash memory, magnetic tape, floppy disk, a hard disk drive, a Compact Disc (CD), a Digital Video Disk (DVD), Blu-Ray Disc, a digital tape, a computer memory, or the like, as well as transmission type medium such as a digital and / or an analog communication medium (e.g., a fiber optic cable, a waveguide, a wired communications link, a wireless communication link (e.g., transmitter, receiver, transceiver, transmission logic, reception logic, etc.). Further non-limiting examples of signal-bearing media include, but are not limited to, DVD-ROM, DVD-RAM, DVD+RW, DVD-RW, DVD-R, DVD+R, CD-ROM, Super Audio CD, CD-R, CD+R, CD+RW, CD-RW, Video Compact Discs, Super Video Discs, flash memory, magnetic tape, magneto-optic disk, MINIDISC, non-volatile memory card, EEPROM, optical disk, optical storage, RAM, ROM, system memory, web server, or the like.

[0119] The detailed description set forth above in connection with the appended drawings, where like numerals reference like elements, are intended as a description of various embodiments of the present disclosure and are not intended to represent the only embodiments. Each embodiment described in this disclosure is provided merely as an example or illustration and should not be construed as preferred or advantageous over other embodiments. The illustrative examples provided herein are not intended to be exhaustive or to limit the disclosure to the precise forms disclosed. Similarly, any steps described herein may be interchangeable with other steps, or combinations of steps, in order to achieve the same or substantially similar result. Generally, the embodiments disclosed herein are non-limiting, and the inventors contemplate that other embodiments within thescope of this disclosure may include structures and functionalities from more than one specific embodiment shown in the figures and described in the specification.

[0120] In the foregoing description, specific details are set forth to provide a thorough understanding of exemplary embodiments of the present disclosure. It will be apparent to one skilled in the art, however, that the embodiments disclosed herein may be practiced without embodying all the specific details. In some instances, well-known process steps have not been described in detail in order not to unnecessarily obscure various aspects of the present disclosure. Further, it will be appreciated that embodiments of the present disclosure may employ any combination of features described herein.

[0121] The present application may include references to directions, such as “vertical,” “horizontal,” “front,” “rear,” “left,” “right,” “top,” and “bottom,” etc. These references, and other similar references in the present application, are intended to assist in helping describe and understand the particular embodiment (such as when the embodiment is positioned for use) and are not intended to limit the present disclosure to these directions or locations.

[0122] The present application may also reference quantities and numbers. Unless specifically stated, such quantities and numbers are not to be considered restrictive, but exemplary of the possible quantities or numbers associated with the present application. Also, in this regard, the present application may use the term “plurality” to reference a quantity or number. In this regard, the term “plurality” is meant to be any number that is more than one, for example, two, three, four, five, etc. The term “about,” “approximately,” etc., means plus or minus 5% of the stated value. The term “based upon” means “based at least partially upon.”

[0123] The principles, representative embodiments, and modes of operation of the present disclosure have been described in the foregoing description. However, aspects of the present disclosure, which are intended to be protected, are not to be construed as limited to the particular embodiments disclosed. Further, the embodiments described herein are to be regarded as illustrative rather than restrictive. It will be appreciated that variations and changes may be made by others, and equivalents employed, without departing from the spirit of the present disclosure. Accordingly, it is expressly intended that all such variations, changes, and equivalents fall within the spirit and scope of the present disclosure as claimed.

[0124] While illustrative embodiments have been illustrated and described, it will be appreciated that various changes can be made therein without departing from the spirit and scope of the invention.

Claims

CLAIMSWe claim:

1. A nanocomposite proximity-pressure sensor (NPPS), configured to measure one or more human biometrics, the NPPS comprising: an electrode; a conductive foam; and an auxetic MWCNTs-embedded frame, wherein the auxetic MWCNTs-embedded frame is positioned around the conductive foam, wherein when the auxetic frame is compressed, it squeezes and exerts a pressure onto the conductive foam.

2. The NPPS of Claim 1 , wherein the conductive foam comprises polyurethane (PU) or other porous polymer.

3. The NPPS of Claim 1 or Claim 2, wherein the electrode is a carbon nanotube-paper (CPC) electrode or a metal electrode .

4. The NPPS of Claim 1 or Claim 3, wherein the conductive foam is a multiwalled carbon nanotubes (MWCNTs) foam or another electrically conductive composite porous foam.

5. The NPPS of any one of Claims 1-4, wherein the auxetic MWCNTs- embedded frame comprises: a first ring; a second ring, wherein the conductive foam is disposed between the first ring and the second ring; and a plurality of arms connecting the first ring and the second ring.

6. The NPPS of Claim 5, wherein the plurality of arms is configured to bend inward in response to pressure.

7. The NPPS of Claim 5 or Claim 6, wherein the plurality of arms is angled between about 10 degrees and 90 degrees.

8. The NPPS of Claim 7, wherein the plurality of arms is angled between about 30 degrees and 90 degrees.

9. The NPPS of Claim 7, wherein the plurality of arms is angled between about50 degrees.

10. The NPPS of any one of Claims 1-9, wherein the human biometrics are selected from respiratory rate, heart rate, sleep posture, sleep pattern, blood pressure, lung volume, heart rate variability, respiratory efforts, proximity, phase difference between abdomen and thorax, thoracic and abdominal movement, thorax-to-abdomen breathing ratio, arterial vessel induced vibration, snoring, body position, ballistocardiography (BCG), sitting posture, or a combination thereof.

11. A sensor array comprising: a plurality of proximity-pressure sensor (NPPS), configured to measure one or more human biometrics, each NPPS comprising: a carbon nanotube-paper (CPC) electrode; a multiwalled carbon nanotubes (MWCNTs) foam; and an auxetic MWCNTs-embedded frame, wherein the auxetic MWCNTs- embedded frame encloses the MWCNTs foam.

12. The sensor array of Claim 11, wherein the plurality of NPPS is four NPPS.

13. The sensor array of Claim 11 or Claim 12, wherein the sensor array is embedded into a mattress.

14. The sensor array of Claim 11 or Claim 12, wherein the sensor array is embedded into a car seat, a wheelchair, a yoga mat, an exercise mat, or a gym floor.

15. The sensor array of any one of Claims 11-14, wherein the sensor array is configured to measure heart rate, respiratory rate, sleep posture, sleep pattern, blood pressure, respiratory biometrics, proximity, ballistocardiography (BCG), sitting posture, or a combination thereof.

16. A blood pressure measurement device comprising:The sensor array of any one of Claims 11-14, wherein the sensor array is a first sensor array configured to measure a chest ballistocardiography (BCG) signal; and a second sensor array configured to measure a leg BCG signal.

17. The blood pressure measurement device of Claim 16, further comprising a blood pressure cuff.

18. The blood pressure measurement device of Claim 16 or Claim 17, further comprising a strain sensor belt.

19. A method of measuring blood pressure with the blood pressure measurement device of any one of Claims 16-18, the method comprising: capturing the chest ballistocardiography (BCG) signal of a patient; capturing the leg BCG signal of the patient; extracting j -peaks from the chest BCG signal and the leg signal; determining a pulse transit time (PTT); and determining blood pressure with a machine learning algorithm based on the PPT.

20. The method of Claim 19, wherein the method further comprises: reducing noise of the leg BCG signal, the chest BCG signal, or both the leg BCG signal and the chest BCG signal by using signal processing methods, wherein the signal processing methods include wavelet decomposition, moving average, Savitzky-Golay filtering, or a combination thereof.

21. The method of Claim 19 or Claim 20, wherein the machine learning algorithm is a Support Vector Machine (SVM) model.

22. A blood pressure measurement device comprising:The sensor array of any one of Claimsl l-15, wherein the sensor array is a first sensor array configured to measure a chest ballistocardiography (BCG) signal; and a second sensor array configured to measure an arm, a head, a finger, or a foot BCG signal.

23. A sleep sensor mattress, comprising: a mattress; a plurality of sensor arrays embedded into the mattress, wherein each sensor array comprises: four nanocomposite proximity-pressure sensor (NPPS), configured to measure one or more human biometrics, each NPPS comprising:a carbon nanotube-paper (CPC) electrode, a multiwalled carbon nanotubes (MWCNTs) foam, and an auxetic MWCNTs-embedded frame, wherein the auxetic MWCNTs-embedded frame encloses the MWCNTs foam; and a sensor communication module.

24. The sleep sensor mattress of Claim 24, wherein the sleep sensor mattress is configured to measure human presence, sleep posture, one or more ballistocardiography (BCG) signals, heart rate, respiratory rate, blood pressure, or a combination thereof.

25. A sleep sensor mattress comprising: four nanocomposite proximity-pressure sensor (NPPS), configured to measure one or more human biometrics, each NPPS comprising: a carbon nanotube-paper (CPC) electrode, a multiwalled carbon nanotubes (MWCNTs) foam, and an auxetic MWCNTs-embedded frame, wherein the auxetic MWCNTs- embedded frame encloses the MWCNTs foam; a microcontroller communicatively coupled to the four NPPS; fourteen sensor arrays, wherein each sensor array comprises four NPPS; and an I2C connection communicatively coupled to the fourteen sensor arrays.

26. A car seat device comprising: a car seat; and the sensor array of Claim 11, wherein the sensor array is embedded into a backrest of the car seat, wherein car seat device is configured to measure a respiratory effort, a heart rate, a ballistocardiography (BCG), or a combination thereof while a vehicle is moving.

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