Human Joint Motion Research Articles

Fabrication and application of flexible piezoelectric sensor based on electrospun PVDF-TrFE nanofibers

Flexible piezoelectric sensors have recently demonstrated great promise for use in wearable electronics and electronic skin. In this study, electrospinning was used to generate piezoelectric PVDF-TrFE nanofiber sensors. The relationship between nanofiber alignment orientation and piezoelectric property was examined. The experimental results demonstrated that the piezoelectric property improved with the increasing of nanofiber alignment orientation. The response time of piezoelectric sensor was 2 ms, and the sensitivity was 0.446 V/N and 0.029 V/N at vibration frequency of 5 Hz and 1 Hz, respectively. In addition, the applications of flexible piezoelectric sensor in human joint movements and surface contact have been demonstrated, indicates the potential in the field of motion monitoring.

Customizable and scalable manufacture of aesthetic ionic conductive silk yarns for e-textile devices

Electronic textiles (e-textiles), which combine the comfort of textiles with the functionality of soft electronics, have attracted much attention in wearable applications. However, scale-up fabrication of stretchable textiles with concurrent high stability and aesthetic appeal features poses significant challenges. Here, we report a scalable and customizable dip-coating strategy for the preparation of multiple-colored and functionally stable ionic conductive silk yarns (i-SY) and demonstrate their effectiveness in exercise posture correction. The i-SY preserves the inherent properties of silk fibers and ionic liquids, exhibiting improved mechanical properties, weaving capabilities, flame retardancy, and electrical conductivity. The method leverages the exceptional transparency of ionic liquids, facilitating the large-scale production of colored i-SYs to meet aesthetic demands. Additionally, the i-SY sensor exhibits exemplary sensing sensitivity and stability, enabling the real-time monitoring of human joint movements and facilitating precise posture correction during fitness exercises. We envision that the proposed sensing textile holds promise for applications in wearable devices, healthcare monitoring, and human–machine interfaces.

High Strength and Toughness Polymeric Triboelectric Materials Enabled by Dense Crystal-Domain Cross-Linking.

Lightweight, easily processed, and durable polymeric materials play a crucial role in wearable sensor devices. However, achieving simultaneously high strength and toughness remains a challenge. This study addresses this by utilizing an ion-specific effect to control crystalline domains, enabling the fabrication of a polymeric triboelectric material with tunable mechanical properties. The dense crystal-domain cross-linking enhances energy dissipation, resulting in a material boasting both high tensile strength (58.0 MPa) and toughness (198.8 MJ m-3), alongside a remarkable 416.7% fracture elongation and 545.0 MPa modulus. Leveraging these properties, the material is successfully integrated into wearable self-powered devices, enabling real-time feedback on human joint movement. This work presents a valuable strategy for overcoming the strength-toughness trade-off in polymeric materials, paving the way for their enhanced applicability and broader use in diverse sensing applications.

From Fluorescence-Transfer-Lightening-Printing-Assisted Conductive Adhesive Nanocomposite Hydrogels toward Wearable Interactive Optical Information-Electronic Strain Sensors.

The interactive flexible device, which monitors the human motion in optical and electrical synergistic modes, has attracted growing attention recently. The incorporation of information attribute within the optical signal is deemed advantageous for improving the interactive efficiency. Therefore, the development of wearable optical information-electronic strain sensors holds substantial promise, but integrating and synergizing various functions and realizing strain-mediated information transformation keep challenging. Herein, an amylopectin (AP) modified nanoclay/polyacrylamide-based nanocomposite (NC) hydrogel and an aggregation-induced-emission-active ink are fabricated. Through the fluorescence-transfer printing of the ink onto the hydrogel film in different strains with nested multiple symbolic information, a wearable interactive fluorescent information-electronic strain sensor is developed. In the sensor, the nanoclay plays a synergistic "one-stone-three-birds" role, contributing to "lightening" fluorescence (≈80 times emission intensity enhancement), ionic conductivity, and excellent stretchability (>1000%). The sensor has high biocompatibility, resilience (elastic recovery ratio: 97.8%), and strain sensitivity (gauge factor (GF): 10.9). Additionally, the AP endows the sensor with skin adhesiveness. The sensor can achieve electrical monitoring of human joint movements while displaying interactive fluorescent information transformation. This research poses an efficient strategy to develop multifunctional materials and provides a general platform for achieving next-generation interactive devices with prospective applications in wearable devices, human-machine interfaces, and artificial intelligence.

Research and experiment on active training of lower limb based on five-bar mechanism of man-machine integration system

AbstractIn view of the fact that the current research on active and passive rehabilitation training of lower limbs is mainly based on the analysis of exoskeleton prototype and the lack of analysis of the actual movement law of limbs, the human-machine coupling dynamic characteristics for active rehabilitation training of lower limbs are studied. In this paper, the forward and inverse kinematics are solved on the basis of innovatively integrating the lower limb and rehabilitation prototype into a human-machine integration system and equivalent to a five-bar mechanism. According to the constraint relationship of hip joint, knee joint and ankle joint, the Lagrange dynamic equation and simulation model of five-bar mechanism under the constraint of human physiological joint motion are constructed, and the simulation problem of closed-loop five-bar mechanism is solved. The joint angle experimental system was built to carry out rehabilitation training experiments to analyze the relationship between lower limb error and height, weight and BMI, and then, a personalized training planning method suitable for people with different lower limb sizes was proposed. The reliability of the method is proved by experiments. Therefore, we can obtain the law of limb movement on the basis of traditional rehabilitation training, appropriately reduce the training speed or reduce the man-machine position distance and reduce the training speed or increase the man-machine distance to reduce the error to obtain the range of motion angle closer to the theory of hip joint and knee joint respectively, so as to achieve better rehabilitation.

Stretchable, Washable, and Anti-Ultraviolet i-Textile-Based Wearable Device for Motion Monitoring.

Smart textiles with multifunction and highly stable performance are essential for their application in wearable electronics. Despite the advancement of various smart textiles through the decoration of conductive materials on textile surfaces, improving their stability and functionality remains a challenging topic. In this study, we developed an ionic textile (i-textile) with air permeability, water resistance, UV resistance, and sensing capabilities through in situ photopolymerization of ionogel onto the textile surface. The i-textile presents air permeability comparable to that of bare textile while possessing enhanced UV resistance. Remarkably, the i-textile maintains excellent electrical properties after washing 20 times or being subjected to 300 stretching cycles at 30% tension. When applied to human joint motion detection, the i-textile-based sensors can effectively distinguish joint motion based on their sensitivity and response speed. This research presents a novel method for developing smart textiles that further advances wearable electronics.

High-conductivity and long-term stability strain sensor based on silk fibroin and polyvinyl alcohol hydrogels

Conductive hydrogels as promising candidates of wearable electronic have attracted considerable interest in health monitoring, multifunctional electronic skins, and human-machine interfaces. However, to simultaneously achieve excellent electrical properties, long-term stability, and water vapor permeability of conductive hydrogels remains a challenge. Herein, a highly conductive hydrogel (MPRS) with high breathability (5.90 mg∙cm−2∙h−1), excellent electrical conductivity (6.67 S/m), high elongation (315%), and remarkable water retention properties was prepared adopting cold freeze-thawing method. MXene, poly (3, 4-ethylenedioxythiophene): poly (styrenesulfonate) (PEDOT: PSS), and reduced graphene oxide were used as conductive materials, while silk fibroin (SF) and polyvinyl alcohol (PVA) were used as the gel matrix. Lithium chloride solution was used as a solvent during the preparation process. The strain sensor based on this hydrogel demonstrates rapid response (0.33 s) and recovery ability (0.31 s) to strain stimuli. Upon 250 cycles of stretching at 35% strain, the output signal remains stable and highly repeatable, ensuring accurate detection of human joint movements and facial expressions. The fabrication of flexible electrodes using the hydrogel enabled real-time monitoring of human electromyography and electrocardiogram signals. The applications in a 2D sensor array and human-machine interaction interfaces also proved the excellent performance of this conductive hydrogels in wearable sensor.

Terrain slope parameter recognition for exoskeleton robot in urban multi-terrain environments

Lower limb augmentation exoskeletons (LLAE) have been applied in several domains to enforce human walking capability. As humans can adjust their joint moments and generate different amounts of mechanical energy while walking on different terrains, the LLAEs should provide adaptive augmented torques to the wearer in multi-terrain environments, which requires LLAEs to implement accurate terrain parameter recognition. However, the outputs of previous terrain parameter recognition algorithms are more redundant, and the algorithms have higher computational complexity and are susceptible to external interference. Therefore, to resolve the above issues, this paper proposed a neural network regression (NNR)-based algorithm for terrain slope parameter recognition. In particular, this paper defined for the first time a unified representation of terrain parameters: terrain slope (TS), a single parameter that can provide enough information for exoskeleton control. In addition, our proposed NNR model uses only basic human parameters and LLAE joint motion posture measured by an Inertial Measurement Unit (IMU) as inputs to predict the TS, which is computationally simpler and less susceptible to interference. The model was evaluated using K-fold cross-validation and the results showed that the model had an average error of only 2.09∘\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^\\circ $$\\end{document}. To further validate the effectiveness of the proposed algorithm, it was verified on a homemade LLAE and the experimental results showed that the proposed TS parameter recognition algorithm only produces an average error of 3.73∘\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^\\circ $$\\end{document} in multi-terrain environments. The defined terrain parameters can meet the control requirements of LLAE in urban multi-terrain environments. The proposed TS parameter recognition algorithm could facilitate the optimization of the adaptive gait control of the exoskeleton system and improve user experience, energy efficiency, and overall comfort.

Hierarchical Spatial Confinement Unlocking the Storage Limit of MoS2 for Flexible High-Energy Supercapacitors.

Molybdenum sulfide (MoS2) is a promising electrode material for supercapacitors; however, its limited Mo/S edge sites and intrinsic inert basal plane give rise to sluggish active electronic states, thus constraining its electrochemical performance. Here we propose a hierarchical confinement strategy to develop ethylene molecule (EG)-intercalated Co-doped sulfur-deficient MoS2 (Co-EG/SV-MoS2) for efficient and durable K-ion storage. Theoretical analyses suggest that the intercalation-confined EG and lattice-confined Co can enhance the interfacial K-ion storage capacity while reducing the K-ion diffusion barrier. Experimentally, the intercalated EG molecules with mildly reducing properties induced the creation of sulfur vacancies, expanded the interlayer spacing, regulated the 2H-1T phase transition, and strengthened the structural grafting between layers, thereby facilitating ion diffusion and ensuring structural durability. Moreover, the Co dopants occupying the initial Mo sites initiated charge transfer, thus activating the basal plane. Consequently, the optimized Co-EG/SV-MoS2 electrode exhibited a substantially improved electrochemical performance. Flexible supercapacitors assembled with Co-EG/SV-MoS2 delivered a notable areal energy density of 0.51 mW h cm-2 at 0.84 mW cm-2 with good flexibility. Furthermore, supercapacitor devices were integrated with a strain sensor to create a self-powered system capable of real-time detection of human joint motion.

TRANSLATIONAL MODELS FOR OSTEOARTHRITIS: SPEEDING UP FROM BENCH TO BEDSIDE

Translational models for OA have used a variety of small (mouse, rat) and large (sheep, pig) animal models to evaluate the efficacy of a specific therapy. Clinical trials based on the results of these animal models have yielded mixed results with respect to the treatment of the disease. Due to greater stringency in EU regulations in the use of animal models for research, ex vivo models of OA (e.g. cartilage explants, bioreactors) are being developed to mimic human joint motion as well as the inflammatory milieu (e.g. IL-1β) that can be used to understand efficacy of therapy in a physiological environment. The development of these models can enable therapies to undergo clinical trials in patients without the necessity for long-term animal studies. This presentation will describe the state of the art in this field and will discuss whether there is potential to speed up translation from bench to bedside in the future.

Concurrent validity of inertial measurement units in range of motion measurements of upper extremity: A systematic review and meta-analysis.

Inertial measurement units (IMUs) have proven to be valuable tools in measuring the range of motion (RoM) of human upper limb joints. Although several studies have reported on the validity of IMUs compared to the gold standard (optical motion capture system, OMC), a quantitative summary of the accuracy of IMUs in measuring RoM of upper limb joints is still lacking. Thus, the primary objective of this systematic review and meta-analysis was to determine the concurrent validity of IMUs for measuring RoM of the upper extremity in adults. Fifty-one articles were included in the systematic review, and data from 16 were pooled for meta-analysis. Concurrent validity is excellent for shoulder flexion-extension (Pearson's r=0.969 [0.935, 0.986], ICC=0.935 [0.749, 0.984], mean difference=-3.19 (p=0.55)), elbow flexion-extension (Pearson's r=0.954 [0.929, 0.970], ICC=0.929 [0.814, 0.974], mean difference=10.61 (p=0.36)), wrist flexion-extension (Pearson's r=0.974 [0.945, 0.988], mean difference=-4.20 (p=0.58)), good to excellent for shoulder abduction-adduction (Pearson's r=0.919 [0.848, 0.957], ICC=0.840 [0.430, 0.963], mean difference=-7.10 (p=0.50)), and elbow pronation-supination (Pearson's r=0.966 [0.939, 0.981], ICC=0.821 [0.696, 0.900]). There are some inconsistent results for shoulder internal-external rotation (Pearson's r=0.939 [0.894, 0.965], mean difference=-9.13 (p<0.0001)). In conclusion, the results support IMU as a viable instrument for measuring RoM of upper extremity, but for some specific joint movements, such as shoulder rotation and wrist ulnar-radial deviation, IMU measurements need to be used with caution.

A flexible piezoresistive pressure sensor based on a perovskite MAPbBr3 nanocrystal-embedded polymer composite

The three-dimensional porous structure of the material is adjusted by adding perovskites, and a flexible pressure sensor is constructed in a “sandwich” structure to detect human facial expression, pulse, breathing, and joint movement.

Investigation of flexible graphene hybrid knitted sensor for joint motion recognition based on convolutional neural network fusion long short-term memory network

Wearable electronics have attracted have attracted widespread attentions for their promising applications in motion monitoring and human-computer interaction. This paper proposes a flexible wearable joint movement intelligent sensing and recognition system to achieve stable and reliable motion feature extraction and recognition. Flexible graphene hybrid knitted sensor were prepared by transferring graphenes (GNs) agent onto stretchable knitted products via a simple spray-drying approach. The small dynamic movement of human joints for the prepared GNs hybrid sensing gloves, elbow pads and knee pads were converted into electrical signals for sensitive detection. The convolutional neural network fusion long short-term memory (CNN-LSTM) network with self-attention mechanism (SAM) is established for feature training and intelligent dynamic recognition of the measured joint information. The interconnected conductive networks endowed knitted sensor with good flexibility and remarkable electrical conductivity of 37 S/m. The unique conductive networks in the fabric offered excellent linearity and repeatable resistance response variation for better detection of joint motion. The resistant signal was analyzed by feature extraction, data correlation capture and time sequence relationship modeling. Finally, the test results show that the proposed CNN-LSTM with SAM network achieves 97%, 96% and 100% correct recognition rates for gesture signals, elbow and wrist signals and knee signals respectively, which is obviously higher than other recognition algorithms. It has great application prospects in the fields of smart wear, medical detection, and smart elderly care.

Multi-role conductive hydrogels for flexible transducers regulated by MOFs for monitoring human activities and electronic skin functions.

Metal organic frameworks (MOFs) have garnered significant attention in the development of stretchable and wearable conductive hydrogels for flexible transducers. However, MOFs used in hydrogel networks have been hampered by low mechanical performance and poor dispersibility in aqueous solutions, which affect the performance of hydrogels, including low toughness, limited self-recovery, short working ranges, low conductivity, and prolonged response-recovery times. To address these shortcomings, a novel approach was adopted in which micelle co-polymerization was used for the ex situ synthesis of Zn-MOF-based hydrogels with exceptional stretchability, robust toughness, anti-fatigue properties, and commendable conductivity. This breakthrough involved the ex situ integration of Zn-MOFs into hydrophobically cross-linked polymer chains. Here the micelles of EHDDAB had two functions, first they uniformly dispersed the Zn-MOFs and secondly they dynamically cross-linked the polymer chains, profoundly influencing the mechanical characteristics of the hydrogels. The non-covalent synergistic interactions introduced by Zn-MOFs endowed the hydrogels with the capacity for high stretchability, high stress, rapid self-recovery, anti-fatigue properties, and conductivity, all achieved without external stimuli. Furthermore, hydrogels based on Zn-MOFs can serve as durable and highly sensitive flexible transducers, adept at detecting diverse mechanical deformations with swift response-recovery times and high gauge factor values. Consequently, these hydrogels can be tailored to function as wearable strain sensors capable of sensing significant human joint movements, such as wrist bending, and motions involving the wrist, fingers, and elbows. Similarly, they excel at monitoring subtle human motions, such as speech pronunciation, distinguishing between different words, as well as detecting swallowing and larynx vibrations during various activities. Beyond these applications, the hydrogels exhibit proficiency in distinguishing and reproducing various written words with reliability. The Zn-MOF-based hydrogels hold promising potential for development in electronic skin, medical monitoring, soft robotics, and flexible touch panels.

A flexible silk film based on synergy of hydrogen bonds and a cross-linking network for magnetic sensitive skin

Flexible silk-based anisotropic magnetoresistance skin is developed and used for monitoring human joint motions.

Fuzzy Synchronization Likelihood Graph in Deep Neural Networks for Human Motion Time Series Analysis.

Variable interactivity is crucial in biological multivariate time series analysis. This research suggests using graph structures to represent such interactions for more explainable decision-making processes. However, measuring the variable interaction in a graph is an open problem with no unique solution. Existing graph construction methods are either computationally costly, require extensive training, or disregard the inherent data nonlinearities and nonstationarity. We propose using the Fuzzy Synchronization Likelihood (FSL) criterion to address these challenges in constructing a graph and examining the qualitative similarity and dependency among variables. We propose applying this strategy to automated rehabilitation exercise evaluations based on human joint motion data. This multivariate time series application benefits from FSL-constructed graphs by offering further insight into the kinematics of joint interactions. Finally, we extend the convolutional layer in the Deep Mixture Density Neural Network (DMDN) to process the FSL-constructed graph, extracting practical information regarding task-based variable dependencies. An ablation study shows that the proposed FSL Graph-based Deep Neural Network (FSLGDN) outperforms its competitive approaches that use linear correlations and human anatomy for graph construction. Results also indicate that task-based consideration of joint motion data interactions is more beneficial than anatomy. Furthermore, the inherent nonstationarity of motion data leads to the extraction of more information than its linear correlation counterpart. Finally, while the proposed approach ranks competitively with a DMDN, the proposed approach's graph construction and representation of feature dependencies are more intuitive, leading to more explainable decision-making processes.

Robust hydrogel sensor with good mechanical properties, conductivity, anti-swelling ability, water tolerance and biocompatibility

Versatile PVA/SG conductive hydrogel with robust mechanical properties, high conductivity, excellent anti-swelling ability, water tolerance, biocompatibility and sensitivity can monitor human joint motions and simulate human skin for writing text on a mobile phone screen.

A self-healing polypyrrole/oxidized natural rubber conductive composite as a building block for highly sensitive strain sensors

Wearable flexible strain sensors have attracted significant attention in human movements due to their high sensitivity, broad strain range and long-term stability. However, the fragility of the sensors limits their widespread applications. Here, a self-healing rubber-based conductive composite with excellent electrical conductivity and high sensitivity are prepared by in-situ polymerization of pyrrole in oxidized deproteinized natural rubber latex. Based on the hydrogen bonds between oxidized deproteinized natural rubber, polypyrrole and polyethylene glycol monolauryl ether, the composites show efficient self-healing behavior (η = 91.1%) under 50 °C for 24 h. The conductive composites containing 5 wt% polypyrrole perform excellent conductivity (3.5 S/m), fast response time (180 ms), low strain detection limit (1%), and long-term reliability (over 1500 cycles at 40% strain). The gauge factors are 3.2 at low strain (<60%) and up to 477.6 at large strain (>430%). Furthermore, the rubber-based conductive composites are fabricated as sensors to accurately monitor a variety of human joint movements (finger, wrist, elbow, and knee bending) and facial expressions (pouting, and winking).

Thread-Embedded-in-PDMS Wearable Strain Sensor for Real-Time Monitoring of Human Joint Motion.

Real-time monitoring of human joint motion holds paramount importance in assessing joint health status, preventing and treating joint diseases, and evaluating physical flexibility and coordination. However, traditional strain sensors face limitations in meeting the substantial strain requirements associated with human joint motion. Recently, there has been considerable attention directed towards flexible strain sensors prepared using pliable substrates combined with silk and cotton fabrics. Nonetheless, these sensors exhibit insufficient linearity across the entire measurement range, thereby compromising the predictability of real joint motion based on the output signal. This paper introduced a flexible strain sensor designed to address this issue by offering an enhanced range and high linearity. Specifically, the core wire of the strain sensor was produced by coating a polybutylene terephthalate thread with conductive carbon ink integrated with carbon nanotubes, encapsulated in a thin layer of polydimethylsiloxane in an "S" configuration. The proposed strain sensor maintained excellent linearity within its strain range of 60%, along with advantages such as rapid response speed and robust durability. On-trial tests further affirmed the sensor's capability to effectively monitor the motion of human joints.

Highly ion-conducting, robust and environmentally stable poly(vinyl alcohol) eutectic gels designed by natural polyelectrolytes for flexible wearable sensors and supercapacitors

Flexible conductive materials have received a lot of attention due to their versatility in wearable devices, etc. Eutectic gels are expected to replace the traditional hydrogel as a new generation of soft conductors. Here, sodium alginate (SA), a polysaccharide polyelectrolyte with surface charge, was ingeniously used to construct the energy dissipation and ion transport network. Thus, a double-network polyvinyl alcohol composite eutectic gel with various physical effects was prepared by salting out and solvent replacement process. The obtained gels showed excellent mechanical properties, frost resistance and stability; the maximum fracture strain and toughness can reach 3.87 MPa and 9.87 MJ/m3, respectively. Surprisingly, the –COO− contained in SA plays an active role in the ion transport, and the conductivity (0.30 S/m) was improved by 5 times. Based on this composite gel, a supercapacitor with a wide operating window of 1.7 V and good cyclic charging and discharging stability (capacitance retention at 82.1 % after 1500 cycles) was prepared. And the prepared sensor can sensitively monitor the movement of human joints and perform handwriting recognition. This strategy realizes the balance between mechanical properties and conductivity of eutectic gel and opens up the application of natural polyelectrolytes in the construction of high-performance ion-transport networks.