Body Motion Detection Research Articles

Novel Linear, Piezoresistive, Auxetic Sensors Coated by AAA Battery Active Carbons with Supreme Sensitivity for Human Body Movement Detection

Herein, a novel strategy for creating low‐cost, sustainable, piezoresistive auxetic sensors using the active carbon in consumed AAA batteries, promoting a circular economy, is presented. An auxetic structure with a fixed Poisson's ratio during the strain is designed for sensing. The sensor substrate is silicone RTV2, and the sensing element is the active carbon in AAA batteries chopped to microscale particles using an ultrasonic wave. The sensor mold is designed using Solidworks software and produced using a computer numerical control device and EdgeCam2014 software. The coating process is performed by spraying the prepared particles on the molded auxetic structure and putting the coated auxetic structure under ultraviolet ray to prepare the final sensor. Sensitivity tests are performed, and the results show that the proposed sensor has a better sensitivity of about 1000% and 410% than the previous mixed and layered composite auxetic counterparts. The proposed sensor has linear sensitivity during the strain (estimated with a line with a slope of 0.64) while previous ones have a nonlinear performance (estimated at least with two lines). The sustainable sensor is implemented to detect the movements of the human body, including the movements of the wrist, finger, elbow, and forearm.

Two-dimensional carbon material incorporated and PDMS-coated conductive textile yarns for strain sensing

AbstractIn recent years, innovative technology based upon conductive textile yarns has undergone rapid growth. Nanocomposite-based wearable strain sensors hold great promise for a variety of applications, but specifically for human body motion detection. However, improving the sensitivity of these strain sensors while maintaining their durability remains a challenge in this arena. In the present investigation, polydopamine-treated and two-dimensional nanostructured material, e.g., reduced graphene oxide (rGO)-coated conductive cotton and polyester yarns, was encapsulated using polydimethylsiloxane (PDMS) to develop robustly wash durable and mechanically stable conductive textile yarns. Flexibility and extensibility of all textile yarns of every stage were analyzed using texture analysis. The chemical interactions essential for measuring coating performance among all components were confirmed by Fourier transform infrared and scanning electron microscopy. The rGO-coated cotton and polyester yarns exhibited an extensibility of 11.77 and 73.59%, respectively. PDMS-coated conductive cotton and polyester yarns also showed an electrical resistance of 12.22 and 20.33 kΩ, respectively, after 10 washing cycles. The PDMS coating layer acted as a physical barrier against impairment of conductivity during washing. Finally, the mechanically stable and flexible conductive textile yarns were integrated into a knitted cotton glove and armband to create a highly stretchable and flexible textile-based strain sensor for measuring finger and elbow movement. Truly wearable garments able to record proprioceptive maps are critical for further developing this field of application.

Flexible multi-modal sensors based on CNT hollow spheres/PDMS composites for human motion recognition and colloid concentration detection

Because flexible sensors with multi-modal responses are essential for applications in the complex environment of wearable products, a unique carbon nanotube (CNT) hollow-sphere scaffold is fabricated using self-assembling of microspheres and microwave irradiation to achieve flexible strain, pressure, and laser sensors. The CNT hollow-sphere scaffold comprising intertwined CNTs is featured with a sophisticated conductive network, large tensile and compressive deformability, intrinsic photothermoelectric nature, and good accommodation for polydimethylsiloxane (PDMS) matrix. Thus, the fabricated CNT hollow spheres/PDMS composite sensors showed good responses to the applied strain (a high gauge factor exceeding 55000 in the strain region of 95–107%, 27 ms response time, and over 20000 cycles durability at 40% strain), pressure (a gauge factor of 0.07–3.15 kPa−1 with a wide sensing range (up to 248 kPa pressure), and laser irradiation (0.066 s−1 at 5 s of 532 nm laser irradiation). Moreover, their uses in body movement detection, healthcare, tactile perception, and colloid concentration detection are demonstrated. It is believed that the CNT hollow spheres/PDMS composite sensors will pave the way for practical applications for future wearable electronic devices, which could recognize human motion as well as sense air quality and disease diagnosis.

Design and fabrication of self-powered flexible P(VDF-TrFE)/ZnO/TiO2 fiber mats as nanogenerator for wearable applications

Polymer-based nanocomposite fibrous membranes are the basis of wearable flexible devices, but the application of high voltage poling, brittle material as dopants, or toxic compounds is the main challenge in the development of such devices. P(VDF-TrFE)/ZnO/TiO2 electrospun nanofiber mats are used in here to fabricate self-poled, lead-free, and flexible piezoelectric nanogenerators. Herein, self-poled, lead free, and flexible high performance piezoelectric nanogenerators (PENGs) are designed using the P(VDF-TrFE)/ZnO/TiO2 electrospun fiber mats. Three phase nanocomposite mats with varying TiO2 concentrations are fabricated using far-field electrospinning method. In this study, we reported the effect of ZnO and TiO2 nanoparticles on the viscoelastic property and piezoelectric output. The electroactive beta (β) phase fraction of P(VDF-TrFE) mats are observed to be enhanced as a result of the dispersion of TiO2 nanoparticles into the three phase nanocomposite films. The dielectric and viscoelastic properties are improved with the addition of TiO2 nanoparticles. The designed piezoelectric device produced short circuit current, open circuit voltage, and piezoelectric peak power of 4.16 μA, 23 V, and 95.68 μW, respectively. These piezoelectric outputs are obtained with the optimization of TiO2 concentration and the PENG devices are subjected to elbow bending, wrist bending, and finger tapping. The piezoelectric output of three phase composite mats is almost 3.6 times higher than PENG made from P(VDF-TrFE) fiber, and the increment in the performance is due to the synergistic effect of ZnO and TiO2 in the P(VDF-TrFE) polymer matrix. PENG is used to display real-time demonstrations of body movement detection and biomechanical energy harvesting to power an LED bulb. The enhanced performance and robustness of the nanogenerator make the electrospun P(VDF-TrFE)/ZnO/TiO2 mat an excellent candidates for sensing and energy harvesting applications.

A Random Forest Approach to Body Motion Detection: Multisensory Fusion and Edge Processing

Low-complexity and privacy-respecting human sensing is a challenging task in smart environments as it requires the orchestration of multiple sensors, low-impact machine learning (ML) methods, and resource-constrained Internet of Things (IoT) devices. Client/server-based architectures are typically employed to support sensor fusion. However, these architectures need data to be moved to/from the cloud or data centers, which is contrary to the fundamental requirement of the IoT applications to limit costs, complexity, memory footprint, processing, and communication resources. In this article, we propose the design and implementation of an integrated edge device targeting human sensing for indoor smart spaces applications envisioned in Industry 5.0 applications. The proposed device implements the cumulative sum (CUSUM) method for data distillation from multiple sensors and adopts a low-complexity random forest algorithm (RFA) to sense and classify body movements: in particular, the device integrates both infrared (IR) and ultrasonic (US) sensors. This article discusses the benefits of the combined use of CUSUM and RFA methods against classical ML approaches in terms of accuracy, complexity, computing time, and storage. The proposed architecture and processing steps are validated experimentally by targeting the fall detection problem in a smart space environment. RFA reduces the complexity by at least three times compared to classical ML tools based on the analysis of space and time features (convolutional neural networks and long short-term memory): processing time is in the order of 0.1 s while accuracy is about 94%.

Interface design of stretchable and environment-tolerant strain sensors with hierarchical nanocellulose-supported graphene nanocomplexes

With the development of wearable electronics, designing a strain sensor with high sensitivity, stretchability, durability and environmental stability is necessary but remains challenging. Herein, a high-performance conductive elastomer is reported by incorporating hierarchical cellulose nanocrystal/graphene (MCNC-GN) nanocomplexes into polydimethylsiloxane (PDMS) matrix. MCNCs serve as the dispersant to form stable MCNC-GN nanocomplexes, and improve their interfacial bonding with PDMS. The composite elastomer possesses excellent tensile strength (∼4.82 MPa), elongation at break (∼142.4 %), electrical conductivity (∼1.0 S m−1), anti-fatigue and environment-tolerant property due to the synergetic entanglement between MCNC-GN and PDMS molecules. It also has a sensitivity (gauge factor of ∼ 173.17), wide sensing range (∼100 %) and long-term durability, which can monitor both small/large-scaled and complex human motions, as well as subtle acoustic vibrations even under harsh conditions. This work provides a promising material platform for full-range human body motion detection and acoustic sensing, demonstrating great potentials in next-generation wearable electronics.

Measuring depression severity based on facial expression and body movement using deep convolutional neural network.

Real-time evaluations of the severity of depressive symptoms are of great significance for the diagnosis and treatment of patients with major depressive disorder (MDD). In clinical practice, the evaluation approaches are mainly based on psychological scales and doctor-patient interviews, which are time-consuming and labor-intensive. Also, the accuracy of results mainly depends on the subjective judgment of the clinician. With the development of artificial intelligence (AI) technology, more and more machine learning methods are used to diagnose depression by appearance characteristics. Most of the previous research focused on the study of single-modal data; however, in recent years, many studies have shown that multi-modal data has better prediction performance than single-modal data. This study aimed to develop a measurement of depression severity from expression and action features and to assess its validity among the patients with MDD. We proposed a multi-modal deep convolutional neural network (CNN) to evaluate the severity of depressive symptoms in real-time, which was based on the detection of patients' facial expression and body movement from videos captured by ordinary cameras. We established behavioral depression degree (BDD) metrics, which combines expression entropy and action entropy to measure the depression severity of MDD patients. We found that the information extracted from different modes, when integrated in appropriate proportions, can significantly improve the accuracy of the evaluation, which has not been reported in previous studies. This method presented an over 74% Pearson similarity between BDD and self-rating depression scale (SDS), self-rating anxiety scale (SAS), and Hamilton depression scale (HAMD). In addition, we tracked and evaluated the changes of BDD in patients at different stages of a course of treatment and the results obtained were in agreement with the evaluation from the scales. The BDD can effectively measure the current state of patients' depression and its changing trend according to the patient's expression and action features. Our model may provide an automatic auxiliary tool for the diagnosis and treatment of MDD.

A self-powered wearable seizure-monitoring/brain-stimulating system for potential epilepsy treatment

A new self-powered wearable body-detecting/brain-stimulating system for monitoring and restraining epilepsy has been fabricated. The system can monitor body motion in real time and transmit the stimulus signals to brain for restraining the epileptic seizures. The system is composed of energy harvesting module, the body motion detection sensor, the data processing center, and neural stimulator. An arch-shaped device made of piezoelectric ceramic transducer (lead zirconate titanate; PZT) as energy harvesting module can convert the mechanical energy generated by body movement into electricity power. The motion detection sensor is made of polyvinylidene fluoride (PVDF) doped with zinc oxide (ZnO) nanostructures, which can detect the tiny movements of the human body and transmit sensing signals to the data processing center. The data processing center can determine the epileptic seizure and generate neural stimulating signals. By stimulating the dentate gyrus/hilus brain region of mice, the total duration of epileptic seizures in mice is significantly reduced by 40–50%. This new self-powered strategy demonstrates the potential of brain-machine-interface system for personalized treatment of brain disease.

Flexible and hydrophobic nanofiber composites with self-enhanced interfacial adhesion for high performance strain sensing and body motion detection

Flexible and electrically conductive fibric (fiber) composite (CFC) strain sensors are promising in smart wearable electronics. Challenges remain to develop CFC with strong interfacial interaction and excellent durability. Here, a facile and cost-efficient method is proposed to fabricate flexible and hydrophobic CFCs with self-enhanced interfacial adhesion based on ultrasonication induced carbon black nanoparticles (CBNPs) embedded onto the swelled polyurethane (PU) nanofiber membranes immersed in a mixed solvent. CBNPs were tightly decorated onto the nanofiber surface, while the electrical conductivity keeps stable during cyclic durability tests. The CBNPs improve the Young’s modulus and tensile strength while maintain the outstanding stretchability of PU membrane. The CBNPs also endow the CFCs with hydrophobicity and photo-thermal conversion property. The stretchable CFC strain sensors show controllable sensitivity and cyclic stability, and can monitor various body motions. This work provides a new route to fabricate low-cost and high-performance strain sensors without using interfacial bonding agents.

Multifunctional sensors for respiration monitoring and antibacterial activity based on piezoelectric PVDF/BZT-0.5BCT nanoparticle composite nanofibers

In clinical practice, combining sensitive and efficient sensors that have antibacterial properties with masks is a convenient way to monitor vital signs. Therefore, developing flexible pressure sensors with high sensitivity and antibacterial properties is the key for such smart devices. In our work, poly (vinylidene fluoride) (PVDF) nanofibers (NFs) with a high piezoelectric phase were fabricated by electrospinning with an optimized spinning voltage and collecting roller speed. Ba(Ti0.8Zr0.2)O3-0.5(Ba0.7Ca0.3)TiO3 (BZT-0.5BCT) nanoparticles (NPs) synthesized by the hydrothermal method were introduced into PVDF NFs to improve their piezoelectric response to external strain. With 20 wt% 0.5BZT-BCT NPs, the PVDF/BZT-BCT fiber composite sensor showed an output voltage up to 6.37 V with superior sensitivity (0.24 V Kpa−1), a short response time (∼50 ms), good durability over a wide time range and a low detection limit (2.50 mg). The sensor was built in a mask that demonstrated high sensitivity in monitoring the respiratory rate as well as antimicrobial resistance to Echerichia coli (E. coli) and Staphylococcus aureus (S. aureus). Furthermore, this composite fiber sensor can also be applied for the detection of body movement. The multifunctional 0.5BZT-BCT/PVDF fiber composite sensor may find clinical applications.

Biomedical Applications using Hand Gesture with Electromyography Control Signal

Wearables developed for human body signal detection receive increasing attention in the current decade. Compared to implantable sensors, wearables are more focused on body motion detection, which can support human–machine interaction (HMI) and biomedical applications. In wearables, electromyography (EMG), force myography (FMG), and electrical impedance tomography (EIT) based body information monitoring technologies are broadly presented. In the literature, all of them have been adopted for many similar application scenarios, which easily confuses researchers when they start to explore the area. Hence, in this article, we review the three technologies in detail, from basics including working principles, device architectures, interpretation algorithms, application examples, merits and drawbacks, to state-of-the-art works, challenges remaining to be solved and the outlook of the field. We believe the content in this paper could help readers create a whole image of designing and applying the three technologies in relevant scenarios. Index Terms : FMG; EMG; EIT; biological signal; human–system interactivities.

Double-Layered Conductive Network Design of Flexible Strain Sensors for High Sensitivity and Wide Working Range.

For flexible strain sensors, the optimization between sensitivity and working range is a significant challenge due to the fact that high sensitivity and high working range are usually difficult to obtain at the same time. Herein, a breathable flexible strain sensor with a double-layered conductive network structure was designed and developed, which consists of a thermoplastic polyurethane (TPU)/carbon nanotube (CNT) layer (as a substrate layer) and a Ag nanowire (AgNW) layer. The TPU/CNT layer is made of electrospinning TPU with CNTs deposited onto the surface of TPU fibers, and the flexible TPU/CNT mat guarantees the integrity of the conductive path under a large strain. The AgNW layer was prepared by depositing different amounts of AgNWs on the surface of the TPU/CNT layer, and the high-conductivity AgNWs offer a low initial resistance. Benefitting from the synergistic two-layer structure, the as-obtained flexible strain sensor exhibits a very high sensitivity (up to 1477.7) and a very wide working range (up to 150%). Besides, the fabricated sensor exhibits fast response (88 ms), excellent dynamical stability (7000 cycles), and excellent breathability. The working mechanism of the strain sensor was further investigated using various techniques (microscopy, equivalent circuit, and thermal effects of current). Furthermore, the as-fabricated flexible strain sensors accurately detect the omnidirectional human motions, including subtle and large human motions. This work provides an efficient approach to achieve the optimization between high sensitivity and large working range of strain sensors, which may have great potential applications in health monitoring, body motion detection, and human-machine interactions.

Large-scale assembly of highly stretchable and conductive polydopamine-generated poly (ethylene terephthalate)/ polyurethane fabric through the self-assembly intercalation strategy for superior sensing

Smart wearable electronic devices with superb sensing performance have aroused superb attention in recent years. However, designing an admirable strain sensor with the excellent balance between superior sensitivity and excellent stretchability remains a major challenge. Herein, we developed a conductive polydopamine-coated poly (ethylene terephthalate)/polyurethane fabric as the wearable strain sensor with the layered generating structure and the cooperative effect of carboxylated carbon nanotubes and reduced graphene oxide via self-assembly intercalation process. Obviously, the cooperative conductive effect of conductive fillers endowed the fabric with a superb integration of tunable strain sensitivity (0–265.48), outstanding electromechanical performance, great stretchability (0–220%), excellent heating performance, and long-term stability and repeatability under different strains over 2500 cycles. Especially, the as-prepared sensor was used for the detection of tiny body movements and vigorous human motions, indicating its further impressive prospect in wearable smart electronics. Therefore, such the proposed sensor with the layered generating structure exhibited remarkable potential for developing the flexible wearable electronic device of integrated sensing property and excellent electromechanical performance.

Self‐Powered Wearable Piezoelectric Monitoring of Human Motion and Physiological Signals for the Postpandemic Era: A Review

AbstractAs society advances, the shift from passive medical care to health management and preventive medical care has become an important issue, with the realization of wearable monitors becoming desirable. In light of the COVID‐19 pandemic, the number of patients who are in urgent need of the monitoring of biological information is increasing. This review focuses on piezoelectric materials and composites that convert kinetic energy into electrical energy to realize self‐powered wearable monitoring sensors, outlining the recent research activity on sensors for use in healthcare monitoring. First, a general description of the principles of piezoelectric monitoring sensors is given. Next, the development status of piezoelectric materials and composites aimed at the application of detecting tiny motions of the human body is introduced, and then the research trends on the detection of larger human body movements are highlighted. Finally, after presenting the performance of current piezoelectric sensors and future research guidelines for developing multifunctional systems in the post COVID‐19 era, the achievements are summarized. Overall, this review will provide guidance to researchers who are seeking to design and develop highly sensitive self‐powered piezoelectric sensors that monitor human motion and physiological signals.

A Review of EMG-, FMG-, and EIT-Based Biosensors and Relevant Human-Machine Interactivities and Biomedical Applications.

Wearables developed for human body signal detection receive increasing attention in the current decade. Compared to implantable sensors, wearables are more focused on body motion detection, which can support human–machine interaction (HMI) and biomedical applications. In wearables, electromyography (EMG)-, force myography (FMG)-, and electrical impedance tomography (EIT)-based body information monitoring technologies are broadly presented. In the literature, all of them have been adopted for many similar application scenarios, which easily confuses researchers when they start to explore the area. Hence, in this article, we review the three technologies in detail, from basics including working principles, device architectures, interpretation algorithms, application examples, merits and drawbacks, to state-of-the-art works, challenges remaining to be solved and the outlook of the field. We believe the content in this paper could help readers create a whole image of designing and applying the three technologies in relevant scenarios.

A Comparison of Video-based Methods for Neonatal Body Motion Detection.

Preterm infants in a neonatal intensive care unit (NICU) are continuously monitored for their vital signs, such as heart rate and oxygen saturation. Body motion patterns are documented intermittently by clinical observations. Changing motion patterns in preterm infants are associated with maturation and clinical events such as late-onset sepsis and seizures. However, continuous motion monitoring in the NICU setting is not yet performed. Video-based motion monitoring is a promising method due to its non-contact nature and therefore unobtrusiveness. This study aims to determine the feasibility of simple video-based methods for infant body motion detection. We investigated and compared four methods to detect the motion in videos of infants, using two datasets acquired with different types of cameras. The thermal dataset contains 32 hours of annotated videos from 13 infants in open beds. The RGB dataset contains 9 hours of annotated videos from 5 infants in incubators. The compared methods include background substruction (BS), sparse optical flow (SOF), dense optical flow (DOF), and oriented FAST and rotated BRIEF (ORB). The detection performance and computation time were evaluated by the area under receiver operating curves (AUC) and run time. We conducted experiments to detect motion and gross motion respectively. In the thermal dataset, the best performance of both experiments is achieved by BS with mean (standard deviation) AUCs of 0.86 (0.03) and 0.93 (0.03). In the RGB dataset, SOF outperforms the other methods in both experiments with AUCs of 0.82 (0.10) and 0.91 (0.05). All methods are efficient to be integrated into a camera system when using low-resolution thermal cameras.

A Single-material-printed, Low-cost design for a Carbon-based fabric strain sensor

The manufacturing of flexible strain sensors for wearable electronics usually requires different conductive materials for the sensing part and the connection part. This increases the complexity, cost, and performance issues due to the mismatch of the thermo-electro-mechanical properties of the materials. Herein, a new design scheme using a single conductive material is presented for a low-cost mass-producible fabric strain sensor, where a carbon/silicone nanocomposite is screen-printed to make both parts. By exploring the dimension effect and modelling of the conductive tracks, and adopting a large difference of over 100 times in aspect ratio, this research makes the electrical response of the fabric strain sensor depend almost exclusively on the sensing part, while its connection part has a low resistance. The sensor exhibits outstanding performance with a wide working range (60% strain), adequate linearity, long fatigue life (∼50,000 cycles), and mechanical robustness, rendering it suitable for human body movement detection. Moreover, the manufacturing process is simple and low-cost ($11 per m2). Thus, the new design scheme overcomes the mismatch issue and provides an important reference value for the design of flexible resistive sensors working in a high resistance range, from ∼ 100 KΩ to several MΩ.

Superhydrophobic, biocompatible and durable nanofiber composite with an asymmetric structure for anisotropic strain sensing and body motion detection

Anisotropic strain sensors show multi-directional sensing that can be used to monitor complex human motions. Aligned electrically conductive nanofiber membranes have been candidates for anisotropic strain sensors. Challenges remain for developing nanofiber composite strain sensors with a large workable strain range, corrosion resistance, excellent durability and biocompatibility. Here, we propose a facile vacuum filtration method to prepare an asymmetric nanofiber composite for the anisotropic strain sensing. The carbon nanofibers (CNF)/polydimethylsiloxane (PDMS) are deposited onto an aligned thermoplastic polyurethane (TPU) nanofiber membrane surface, forming a two layered structure. The PDMS ensures strong interfacial adhesion between the TPU nanofibers and the CNF layer, which endows the nanofiber composite with excellent surface stability and durability. The superhydrophobic CNF/PDMS constructs the conductive network, while the aligned nanofiber layer provides the anisotropic mechanical deformation. The nanofiber composite is cytotoxicity-free and exhibits excellent biocompatibility and biosafety. The asymmetric nanofiber composite shows anisotropy in the mechanical property and sensing behavior. The nanofiber composite demonstrates a large working strain range and outstanding sensing durability, and can be used for monitoring the multidirectional body joint motions even under corrosive conditions. Furthermore, the conductive CNF/PDMS exhibits excellent photothermal conversion performance and a negative temperature coefficient (NTC) behavior, and is hence suitable for isotropic temperature sensing. The asymmetric structure opens a new avenue for multifunctional and anisotropic strain sensing.

A sensitive and flexible interdigitated capacitive strain gauge based on carbon nanofiber/PANI/silicone rubber nanocomposite for body motion monitoring

Stretchable nanocomposites-based strain gauges have received much attention due to their adjustable properties in various applications, including soft robotics, human health monitoring, body motion detection, structural health monitoring, and artificial intelligence. Although low sensitivity (gauge factor) is one of the challenges of capacitive strain gauges, in this study, we design, manufacture, and illustrate characterizations of a stretchable interdigitated capacitive strain gauge based on carbon nanofiber/polyaniline/silicone rubber nanocomposite by an improvement in sensitivity with linearity, and low hysteresis. This strain gauge reaches a gauge factor of about 14 over an applied strain of 2% and about 2.8 over an applied strain of 20% and demonstrates linearity with negligible hysteresis. The sensitivity of the strain sensor is enhanced not only by the interdigitated design of electrodes but also by the electrodes’ outstanding electrical conductivity, even in a large strain. Due to its sensitivity, the proposed device is suitable for detecting small and large strains and can be used in wearable applications or straight on the skin for human motion detection.

Self-Healing, Wet-Adhesion silk fibroin conductive hydrogel as a wearable strain sensor for underwater applications

Wearable strain sensors based on conductive hydrogels have attracted much attention due to their great application potential in fields such as human motion and health monitoring. Under the guidance of application, the design of conductive hydrogels with multiple functions has been developed. However, due to the weakening or elimination of some properties of hydrogels by water molecules, the design of wearable sensors suitable for use in wet or underwater environments and with biosafety is still a challenge. In this paper, the conductive polymer polypyrrole (PPy) combined with silk fibroin (SF) and tannic acid (TA) were introduced into the same gel network by in situ polymerization, and the SF/TA@PPy conductive hydrogel was successfully constructed. The hydrogel has stretchability, skin compliance, antibacterial and biocompatibility properties. In air or underwater, SF/TA@PPy can perform self-healing of mechanical properties, electrical properties, and sensing properties without external stimulation. At the same time, the SF/TA@PPy showed excellent wet adhesion to various materials. It has a wide working range, high strain sensitivity and fast resistance response. It still has stable electrical performance after multiple stretching cycles. As a wearable strain sensor, the SF/TA@PPy can directly adhere to the surface of the human body without the aid of external objects. In addition, it can quickly perceive large strain body movements such as finger joints, wrist joints, elbow joints and knee joints. Changes in small-strain physiological signals such as smile, frown, cough and respiration can also be accurately captured. Body motion detection can be carried out not only in air but also in water. Based on stable electrical properties, SF/TA@PPy can achieve effective underwater information transmission by controlling body movement changes to express the Morse code.