Human Motion Research Articles

Validity and Reliability of OpenPose-Based Motion Analysis in Measuring Knee Valgus during Drop Vertical Jump Test.

OpenPose-based motion analysis (OpenPose-MA), utilizing deep learning methods, has emerged as a compelling technique for estimating human motion. It addresses the drawbacks associated with conventional three-dimensional motion analysis (3D-MA) and human visual detection-based motion analysis (Human-MA), including costly equipment, time-consuming analysis, and restricted experimental settings. This study aims to assess the precision of OpenPose-MA in comparison to Human-MA, using 3D-MA as the reference standard. The study involved a cohort of 21 young and healthy adults. OpenPose-MA employed the OpenPose algorithm, a deep learning-based open-source two-dimensional (2D) pose estimation method. Human-MA was conducted by a skilled physiotherapist. The knee valgus angle during a drop vertical jump task was computed by OpenPose-MA and Human-MA using the same frontal-plane video image, with 3D-MA serving as the reference standard. Various metrics were utilized to assess the reproducibility, accuracy and similarity of the knee valgus angle between the different methods, including the intraclass correlation coefficient (ICC) (1, 3), mean absolute error (MAE), coefficient of multiple correlation (CMC) for waveform pattern similarity, and Pearson's correlation coefficients (OpenPose-MA vs. 3D-MA, Human-MA vs. 3D-MA). Unpaired t-tests were conducted to compare MAEs and CMCs between OpenPose-MA and Human-MA. The ICCs (1,3) for OpenPose-MA, Human-MA, and 3D-MA demonstrated excellent reproducibility in the DVJ trial. No significant difference between OpenPose-MA and Human-MA was observed in terms of the MAEs (OpenPose: 2.4° [95%CI: 1.9-3.0°], Human: 3.2° [95%CI: 2.1-4.4°]) or CMCs (OpenPose: 0.83 [range: 0.99-0.53], Human: 0.87 [range: 0.24-0.98]) of knee valgus angles. The Pearson's correlation coefficients of OpenPose-MA and Human-MA relative to that of 3D-MA were 0.97 and 0.98, respectively. This study demonstrated that OpenPose-MA achieved satisfactory reproducibility, accuracy and exhibited waveform similarity comparable to 3D-MA, similar to Human-MA. Both OpenPose-MA and Human-MA showed a strong correlation with 3D-MA in terms of knee valgus angle excursion.

A robust antagonistically assembled triboelectric nanogenerator based on dead‐end closed pore through a one‐step improved phase separation

AbstractThe ongoing global energy crisis presents a significant challenge all over the world. In this study, triboelectric nanogenerator (TENG) made of recyclable thermoplastic polycarbonate has been proposed as a promising environmentally friendly solution due to its potential capability to convert low‐frequency mechanical energy sources such as human motion and ocean waves into electricity with potential applications including powering wearable electronic devices and self‐powered sensors. We introduce an antagonistically structured TENG (A‐TENG) composed of honeycomb porous polycarbonate (hc‐PC) and convex‐patterned dimethylsiloxane, employing a novel one‐stage improved phase separation method. This A‐TENG overcomes the demerits of others thanks to its high surface contact area, simplified fabrication process, enhanced energy conversion efficiency, and cost‐effectiveness, requiring minimal investment. More importantly, this work utilized hc‐PC as a framework for slippery liquid‐infused porous surface (SLIPS) based TENG owing to the special honeycomb porous structure to impregnate silicon oil within pore arrays and mechanically support the whole system. The practical application of SLIPS‐TENG was demonstrated while deployed on the house rooftop to harvest raindrop energy and act as a rain detection sensor.

High-Performance Stretchable Strain Sensors Based on Auxetic Fabrics for Human Motion Detection.

Wearable strain sensors play a pivotal role in real-time human motion detection and health monitoring. Traditional fabric-based strain sensors, typically with a positive Poisson's ratio, face challenges in maintaining sensitivity and comfort during human motion due to conflicting resistance changes in different strain directions. In this work, high-performance stretchable strain sensors are developed based on graphene-modified auxetic fabrics (GMAF) for human motion detection in smart wearable devices. The proposed GMAF sensors, with a negative Poisson's ratio achieved through commercially available warp-knitting technology, exhibit an 8-fold improvement in sensitivity compared to conventional plain fabric sensors. The unique auxetic fabric structure enhances sensitivity by synchronizing resistance changes in both wale and course directions. The GMAF sensors demonstrate excellent washability, showing only slight degradation in auxeticity and an acceptable increase in resistance after 10 standard wash cycles. The GMAF sensors maintain stability under different strain levels and various motion frequencies, emphasizing their dynamic performance. The sensors exhibit superior conformability to joint movements, which effectively monitor a full range of motions, including joint bending, sports activities, and subtle actions like coughing and swallowing. The research underscores a promising approach to achieve industrial-scale production of wearable sensors with improved performance and comfort through fabric structure design.

Integration of multiscale fusion of residual neural network with 2-D gramian angular fields for lower limb movement recognition based on multi-channel sEMG signals

The human lower limb movements recognition (LLMR) plays a pivotal role in active lower limb exoskeleton robots. Employing surface electromyography (sEMG) signals for LLMR allows for the convenient, rapid and stable capture of signal variations, facilitating efficient identification of lower limb motion patterns. However, current sEMG-based LLMR methods face challenges such as incomplete feature extraction, limited contextual information and restricted feature extraction scales during feature extraction. This paper proposed a LLMR method based on Gramian Angular Fields (GAF) and multiscale fusion of Residual Neural Network (MS-ResNet). The denoised sEMG time series was transformed into Gramian Angular Difference Field (GADF) matrix based on GAF. The MS-ResNet model, incorporating ResNet and multiscale feature fusion concepts, was proposed to comprehensively capture global and local information through different-scale feature extraction and fusion, so as to improve recognition performance. sEMG signals from 11 muscles of the preferred leg of 15 healthy subjects were recorded during six common lower limb movements. Experimental analysis investigated the impact of the convolutional kernel size (k × k) in Stream 2 of MS-ResNet and the number of muscles involved on recognition performance. The study revealed that selecting k as 13, coupled with 11 muscles, yielded optimal model performance with the average cross-individual recognition accuracy reaching 97.62 %, demonstrating the model’s efficiency in LLMR. This method could provide a viable solution for developing more efficient and reliable LLMR systems, applicable to lower limb exoskeleton robots and intelligent prosthetics.

Design of self-healing nanocomposite hydrogels and the application to the detection of human exercise and ascorbic acid in sweat

Hydrogel sensors have broad application prospects in human motion monitoring and sweat composition detection. However, hydrogel-based sensors are faced with challenges such as low accuracy and poor mechanical properties of analytes detection. Based on mussel-inspired chemistry, we synthesized mesoporous silica@polydopamine-Au (MPS@PDA-Au) nanomaterials and designed a self-healing nanocomposite hydrogel to monitor human movement and ascorbic acid detection in sweat. Mesoporous silica (MPS) possess orderly mesoporous structure. Dopamine (DA) polymerized on the surface of MPS to generate polydopamine (PDA), forming the composite material MPS@PDA-Au. This composite was then embedded into polyvinyl alcohol (PVA) hydrogels through a simple freeze-thaw cycle process. The hydrogels have achieved excellent deformable ability (508.6%), self-healing property (90.5%) and mechanical strength (2.9 MPa). The PVA/MPS@PDA-Au hydrogel sensors had the characteristics of fast response time (123.2 ms), wide strain sensing range (0–500%), excellent fatigue resistance and stability in human detection. The detection range of ascorbic acid (AA) in sweat was wide (8.0 μmol/L-100.0 μmol/L) and the detection limit was low (3.3 μmol/L). Therefore, these hydrogel sensors have outstanding application prospects in human motion monitoring and sweat composition detection.

A high sensing performance piezoresistive sensor based on TPU/c-MWCNTs/ V2CTX-MXene electrospun film for human motion detection

Since wearable pressure sensors have promising applications in the fields of human physiological signal detection and bionic robots, there is an urgent need for wearable pressure sensors with comprehensive attributes such as high sensitivity and wide detection range. Here, we introduce a facile electrospinning process to fabricate a film composed of thermoplastic polyurethane/carboxyl multiwalled carbon nanotube/V2CTX-MXene (TPU/c-MWCNTs/V2C). This film has high sensitivity and great linearity, designed for flexible piezoresistive sensors. To further enhance sensor performance, we applied a spin-coating of polydimethylsiloxane (PDMS) on the sandpaper surface to create a top substrate with intermittent spinosum structures. Additionally, a layer of TPU fibers was electrospun between the conductive film and the interdigitated electrodes. The assembled components form a flexible piezoresistive sensor with a wide detection range of 0–200 kPa, a high sensitivity of 545.2 kPa−1, a low detection limit of 5 Pa, a short response time of 48 ms, and excellent long-term durability after 7000 loading/unloading cycles, making the piezoresistive sensor highly discriminating for detecting complex human movements. Thanks to the great sensing performance and stable structure, we conducted various demonstrations to capture human physiological signals, including signals of muscle activity and pulse. These findings indicate the potential of the designed flexible pressure sensor for applications in electronic skin and smart devices.

Enhancing social robot's direct gaze expression through vestibulo-ocular movements

Non-verbal communication, especially eye contact, plays a crucial role in human interaction. Integrating eye contact capabilities into robotic behavior presents challenges that require comprehensive interdisciplinary collaboration from the fields of vision science and robotics. This study introduces an innovative methodology to improve the user's perception of the tabletop social robot Haru's direct gaze by explaining how eye and head movements can be managed through simulating visual perception processing. By considering the mechanisms of eye and head movements during visual perception processing, the robot's eye and head gaze behaviors are intended to replicate human gaze movements with Vestibulo-Ocular Movement (VOM) during face-to-face interaction, ultimately enhancing the user's perception of the robot's direct gaze.

Hydrogen bonds-pinned entanglement double network alginate hydrogel for electrical application

In response to prevailing challenges encountered in electrical applications, including insufficient mechanical strength, subpar tensile properties, and limited adaptability to dynamic motion environments, we engineered a pioneering hydrogel adhesive. Simultaneously, we presented a novel interpretation of the application of ZnO in hydrogels. Our innovative approach entailed the intertwining of polyvinyl alcohol (PVA) and flexible sodium alginate (SA) double networks (DN) through cross-linking mechanisms, resulting in the formation of a hydrogen-bonding pinned DN hydrogel. This groundbreaking design substantially amplified the cohesive and adhesive properties of the hydrogel, while the incorporation of zinc oxide (ZnO) through modification served to enhance its electrical conductivity. Our hydrogel sensor demonstrated exceptional capabilities in monitoring human motion, adeptly meeting the demands of diverse motion scenarios. Furthermore, meticulous consideration had been given to the influence of perspiration on sensor performance, rendering our sensor exceptionally well-suited for real-world applications.

Facile One-Pot Preparation of Polypyrrole-Incorporated Conductive Hydrogels for Human Motion Sensing.

Conductive hydrogels have been widely used in soft robotics, as well as skin-attached and implantable bioelectronic devices. Among the candidates of conductive fillers, conductive polymers have become popular due to their intrinsic conductivity, high biocompatibility, and mechanical flexibility. However, it is still a challenge to construct conductive polymer-incorporated hydrogels with a good performance using a facile method. Herein, we present a simple method for the one-pot preparation of conductive polymer-incorporated hydrogels involving rapid photocuring of the hydrogel template followed by slow in situ polymerization of pyrrole. Due to the use of a milder oxidant, hydrogen peroxide, for polypyrrole synthesis, the photocuring of the hydrogel template and the growing of polypyrrole proceeded in an orderly manner, making it possible to prepare conductive polymer-incorporated hydrogels in one pot. The preparation process is facile and extensible. Moreover, the obtained hydrogels exhibit a series of properties suitable for biomedical strain sensors, including good conductivity (2.49 mS/cm), high stretchability (>200%), and a low Young's modulus (~30 kPa) that is compatible with human skin.

Ultrastretchable, Ultralow Hysteresis, High-Toughness Hydrogel Strain Sensor for Pressure Recognition with Deep Learning.

Hydrogel, as a promising material for a wide range of applications, has demonstrated considerable potential for use in flexible wearable devices and engineering technologies. However, simultaneously realizing the ultrastretchability, low hysteresis, and high toughness of hydrogels is still a great challenge. Here, we present a dual physically cross-linked polyacrylamide (PAM)/sodium hyaluronate (HA)/montmorillonite (MMT) hydrogel. The introduction of HA increases the degree of chain entanglement, and the addition of MMT acts as a stress dissipation center and cross-linking agent, resulting in a hydrogel with high toughness and low hysteretic properties. This hydrogel synthesized by a simple strategy exhibited ultrahigh stretchability (3165%), high breaking stress (228 kPa), high toughness (4.149 MJ/m3), and ultralow hysteresis (≈2% at 100% of strain). The fabricated hydrogel flexible strain sensors possessed fast response and recovery times (62.5:75 ms), a wide strain detection range (2000%), a strain detection limit of 1%, and excellent cycling stability over 500 cycles. Furthermore, the hydrogel flexible strain sensor can be used for human motion monitoring, gesture recognition, and pressure recognition assisted by deep learning algorithms, showing enormous promise for applications.

Utilizing deep learning models in an intelligent spiral drawing classification system for Parkinson's disease classification.

Parkinson's disease (PD) is a neurodegenerative illness that impairs normal human movement. The primary cause of PD is the deficiency of dopamine in the human brain. PD also leads to several other challenges, including insomnia, eating disturbances, excessive sleepiness, fluctuations in blood pressure, sexual dysfunction, and other issues. The suggested system is an extremely promising technological strategy that may help medical professionals provide accurate and unbiased disease diagnoses. This is accomplished by utilizing significant and unique traits taken from spiral drawings connected to Parkinson's disease. While PD cannot be cured, early administration of drugs may significantly improve the condition of a patient with PD. An expeditious and accurate clinical classification of PD ensures that efficacious therapeutic interventions can commence promptly, potentially impeding the advancement of the disease and enhancing the quality of life for both patients and their caregivers. Transfer learning models have been applied to diagnose PD by analyzing important and distinctive characteristics extracted from hand-drawn spirals. The studies were carried out in conjunction with a comparison analysis employing 102 spiral drawings. This work enhances current research by analyzing the effectiveness of transfer learning models, including VGG19, InceptionV3, ResNet50v2, and DenseNet169, for identifying PD using hand-drawn spirals. Transfer machine learning models demonstrate highly encouraging outcomes in providing a precise and reliable classification of PD. Actual results demonstrate that the InceptionV3 model achieved a high accuracy of 89% when learning from spiral drawing images and had a superior receiver operating characteristic (ROC) curve value of 95%. The comparison results suggest that PD identification using these models is currently at the forefront of PD research. The dataset will be enlarged, transfer learning strategies will be investigated, and the system's integration into a comprehensive Parkinson's monitoring and evaluation platform will be looked into as future research areas. The results of this study could lead to a better quality of life for Parkinson's sufferers, individualized treatment, and an early classification.

A Double-Layer Polyurethane Electrospun Membrane with Directional Sweat Transport Ability for Use as a Soft Strain Sensor.

Wearable electronics for long-term monitoring of physiological signals should be capable of removing sweat generated during daily motion, which significantly impacts signal stability, human comfort, and safety of the electronics. In this study, we developed a double-layer polyurethane (PU) membrane with sweat-directional transport ability that can be applied for monitoring strain signals. The PU membrane was composed of a hydrophilic, conductive layer and a relatively hydrophobic layer. The double-layer PU composite membrane exhibited varied pore size and opposite hydrophilicity on its two sides, enabling the spontaneous pumping of sweat from the hydrophobic side to the hydrophilic side, i.e., the directional transport of sweat. The membrane can be used as a strain sensor to monitor motion strain over a broad working range of 0% to 250% with high sensitivity (GF = 4.11). The sensor can also detect simple human movements even under sweating conditions. We believe that the strategy demonstrated here will provide new insights into the design of next-generation strain sensors.

MXene/MWCNTs-based capacitive pressure sensors combine high sensitivity and wide detection range for human health and motion monitoring

Wearable pressure sensors with exceptional performance are playing a crucial role in various fields such as electronic skin, human motion monitoring, medical diagnosis, and human-computer interaction. However, achieving both high sensitivity and a wide sensing range with simple fabrication and low cost has proven to be a significant challenge for sensors. In this study, a simple capacitive pressure sensor is proposed that multi-walled carbon nanotubes (MWCNTs) are introduced into MXene (Ti3C2TX) as a ‘bridge’. Then the electrode which has excellent conductivity is obtained by filtration of MXene solution and MXene/MWCNTs solution at intervals. Additionally, green and degradable cellulose paper is used as the dielectric layer. The fabricated pressure sensor exhibits a high sensitivity of 4.7 kPa−1 for low pressure from 0 to 1 kPa, a wide detection range of 0–700 kPa, ultra-fast response and recovery times of 46 ms and 62 ms, respectively, and an extremely low detection limit of 0.32 Pa. The sensor remains stable even after 4500 cycles and can accurately monitor the movement of the human joints. Furthermore, it can track human physiological signals, which is beneficial for medical diagnosis and disease prevention, and has significant potential for application in the wearable technology field.

Incorporating shape dependent power law in motion planning for drawing robots

Incorporating human natural features into the algorithmic functions of robots can enhance performance efficiency. One of the most popular features of human movements is the power law that defines the connection between movement speed and path curvature. To contribute to this area, by discussing how the power law can provide a reasonable balance between velocity and efficiency, we propose a novel method to design motion profiles based on the power law. The novelty of this solution lies in the adjustment approach for the power law. In this work, inspired by features of human hand movements, the overall curvature of non-circular shapes is considered as the shape-dependent criterion for motion planning. Also, a framework to apply the proposed approach to any open non-circular curvy contours is presented. To investigate the efficiency of the approach, we considered a simple drawing robot. The simulation and experimental results verify the efficacy of the proposed motion planning method.

Rapid photo-crosslinking, highly conductive, and anti-freezing acrylamide-based hydrogels applied for ECG sensors

In recent years, ionic conductive hydrogels have attracted significant attention in the wearable sensors field due to their simplified preparation process, cost- effectiveness, and high conductivity. However, a large amount of water in traditional hydrogels inevitably condense into ice at low temperatures and make them lose various properties in their practical applications. Hence, this study proposes a method to rapidly synthesized a water/glycerol binary anti-freezing hydrogel only in 10 s through photo-initiated free-radical polymerization. The introduction of LiCl into the crosslinked network composed of acrylamide (AAm) and poly (ethylene glycol) diacrylate (PEGDA) regulated the ionic conductivity of the hydrogel. The hydrogel exhibits a fracture strain of 1062.1 %, a tensile strength of 31 kPa, and an adhesive strength of 6.49 kPa. The synergistic effects of glycerol and LiCl imparted excellent anti-freezing properties (−60 °C). The three-dimensional network structure of the hydrogel facilitates ion transport, exhibiting superior conductivity (30.25 S/m), maintaining high conductivity capability of 1.46 S/m even at a low temperature of −55 °C. Notably, the hydrogel demonstrates prolonged durability and stability in real-time human motion monitoring applications while concurrently exhibiting remarkable sensitivity as an ECG sensor. Hence, this as-prepared hydrogel can adapt to extreme environments and deliver comprehensive performance as an ECG sensor.

BLUE SABINO: Development of a BiLateral Upper-Limb Exoskeleton for Simultaneous Assessment of Biomechanical and Neuromuscular Output

Arm and hand function play a critical role in the successful completion of everyday tasks. Lost function due to neurological impairment impacts millions of lives worldwide. Despite improvements in the ability to assess and rehabilitate arm deficits, knowledge about underlying sources of impairment and related sequela remains limited. The comprehensive assessment of function requires the measurement of both biomechanics and neuromuscular contributors to performance during the completion of tasks that often use multiple joints and span three-dimensional workspaces. To our knowledge, the complexity of movement and diversity of measures required are beyond the capabilities of existing assessment systems. To bridge current gaps in assessment capability, a new exoskeleton instrument is developed with comprehensive bilateral assessment in mind. The development of the BiLateral Upper-limb Exoskeleton for Simultaneous Assessment of Biomechanical and Neuromuscular Output (BLUE SABINO) expands on prior iterations toward full-arm assessment during reach-and-grasp tasks through the development of a dual-arm and dual-hand system, with 9 active degrees of freedom per arm and 12 degrees of freedom (six active, six passive) per hand. Joints are powered by electric motors driven by a real-time control system with input from force and force/torque sensors located at all attachment points between the user and exoskeleton. Biosignals from electromyography and electroencephalography can be simultaneously measured to provide insight into neurological performance during unimanual or bimanual tasks involving arm reach and grasp. Design trade-offs achieve near-human performance in exoskeleton speed and strength, with positional measurement at the wrist having an error of less than 2 mm and supporting a range of motion approximately equivalent to the 50th-percentile human. The system adjustability in seat height, shoulder width, arm length, and orthosis width accommodate subjects from approximately the 5th-percentile female to the 95th-percentile male. Integration between precision actuation, human–robot-interaction force-torque sensing, and biosignal acquisition systems successfully provide the simultaneous measurement of human movement and neurological function. The bilateral design enables use with left- or right-side impairments as well as intra-subject performance comparisons. With the resulting instrument, the authors plan to investigate underlying neural and physiological correlates of arm function, impairment, learning, and recovery.

Flexible and robust polyaniline/cross-linked collagen sponge with fibrils network structure as a piezoresistive sensing material

The polyaniline/cross-linked collagen sponge (PANI/CCS) was synthesized by polymerizing PANI onto the collagen skeleton using mesoscopic collagen fibrils (CFs) as building blocks, serving as a piezoresistive sensing material. The structure and morphology of PANI/CCS were characterized using scanning electron microscopy (SEM), Fourier infrared spectroscopy (FTIR), X-ray photoelectron spectroscopy (XPS), and thermal analysis (TA). The mechanical properties of PANI/CCS could be controlled by adjusting the CFs content and polymerization conditions. PANI/CCS treated with pure water exhibited exceptional compressive elasticity under 1000 compression cycles, demonstrating a wide strain range (0–85 %), rapid response time (200 ms), recovery time (90 ms), and high sensitivity (6.72 at 40–50 % strain). The treatment of the ionic liquid further improved the elasticity and the strain sensing range (0–95 %). The presence of PANI nanoparticles and mesoscopic collagen fibrils imparted antibacterial properties, stability in solvents, and biodegradability to PANI/CCS. Utilizing PANI/CCS as a piezoresistive sensing material enabled monitoring human movement behavior through the assembled sensor, showing significant potential for flexible wearable devices.

Highly conductive copper-coated polyamide yarn for wearable sensing and Joule heating applications

A highly conductive yarn was developed by electroless deposition of copper metal particles on the polyamide 6 yarn. The successful incorporation of Cu particles into the substrate was revealed by surface and elemental characterizations. The Cu/PA 6 yarn exhibited superior electrical conductivity (2.3 Ω/cm) with improved mechanical properties. The excellent electromechanical properties demonstrated by the Cu/PA 6 composite revealed its sensing capacity for a wide range of strains. Therefore, the composite yarn was affixed to the different regions of the human body and was capable of successfully monitoring various human motions, including finger bending, wrist bending, knee bending, drinking, and writing. In addition, rapid heat generation with high and uniform surface temperature (66.3 °C at 4.0 V in 45 s) validated the potential of the Cu/PA 6 yarn for wearable thermal heating devices. Moreover, rapid heating at a lower voltage, when attached to a wristband, revealed the capability of the composite yarn for localized heating or region-specific temperature regulation for personalized healthcare or comfort. This study informs a facile and effective strategy for developing robust yarn-shaped e-textiles for advanced wearable sensing and thermal heating.

Mussel-inspired adhesion for layer-by-layer assembly towards multi-functional electrically conductive cotton fabric

Layer-by-layer (LBL) assembly has been widely utilized for constructing functional coatings to fabricate versatile materials. However, challenges arise when the assembly building blocks are electrically neutral. To address this issue, we report the use of mussel-inspired adhesion as a unique driving force for LBL assembly to fabricate electrically conductive cotton fabric (ECCF) with carbon nanotubes & graphene as conductive materials and poly(vinyl alcohol-graft-3,4-dihydroxybenzyl acetal) (PVD) as a mussel-inspired adhesive. The robust adhesion provided by PVD endowed the ECCF with excellent stability against sandpaper friction, tape peeling, and water washing. The use of the ECCF in some advanced application such as strain sensing, temperature sensing, and electric heating was studied in detail. The ECCF exhibited fast response and remarkable sensitivity in strain sensing, enabling real-time detection of various human motions such as bending, twisting, and swallowing. In temperature sensing, the ECCF exhibited a negative temperature coefficient effect and offered a linear response within the temperature range of 30–100 °C. In addition, the ECCF demonstrated exceptional electric heating performance, leading to potential use in outdoor insulation and thermal rehabilitation therapy. This innovative application of mussel-inspired adhesion in LBL assembly not only paves the way for advanced smart fabrics but also enhances functionality.

Recyclable and Stable Strain Sensors Based on Semi‐Wrapped Structure of Silver Nanowires in Polyvinyl Alcohol for Human Motion Monitoring

AbstractHighly sensitive strain sensors have been widely used in human motion monitoring, medical treatment, soft robots, and human–computer interaction, and the recycling of functional materials is in a huge demand for eco‐friendly and sustainable electronics. However, the manufacturing of recyclable strain sensors still remains challenging. Here, a semi‐wrapped structure based on silver nanowires and polyvinyl alcohol is proposed to realize a recyclable and stable strain sensor. It has shown excellent sensitivity, fast response, high stretchability and good environmental stability, and is successfully applied for human motion monitoring. In addition, a simple strategy is developed to effectively recycle silver nanowires in an eco‐friendly manner. The recyclable and stable strain sensor demonstrates potential applications in wearable and stretchable electronics, and the recycling strategy can be extended to other noble metal nanomaterials.