Motion Recognition Research Articles

Deep learning algorithms for the recognition of human movements in work activities

Deep learning algorithms for the recognition of human movements in work activities

Leader-Follower Control of Rehabilitative Soft Glove Based on Collaborative Sensing and Fine Motion Recognition

Leader-Follower Control of Rehabilitative Soft Glove Based on Collaborative Sensing and Fine Motion Recognition

Deep learning neural network-assisted badminton movement recognition and physical fitness training optimization

This work aims to solve the problem of low accuracy in recognizing the trajectory of badminton movement. This work focuses on the visual system in badminton robots and conducts side detection and tracking of flying badminton in two-dimensional image plane video streams. Then, the cropped video images are input into a convolutional neural network frame by frame. By adding an attention mechanism, it helps identify the badminton movement trajectory. Finally, to address the detection challenge of flying badminton as a small target in video streams, the deep learning one-stage detection network, Tiny YOLOv2, is improved from both the loss function and network structure perspectives. Moreover, it is combined with the Unscented Kalman Filter algorithm to predict the trajectory of badminton movement. Simulation results show that the improved algorithm performs excellently in tracking and predicting badminton trajectories compared with the existing algorithms. The average accuracy of the proposed method for tracking badminton trajectories is 91.40 %, and the recall rate is 84.60 %. The average precision, recall, and frame rate of the measured trajectories in four simple and complex scenarios of badminton flight video streams are 96.7 %, 95.7 %, and 29.2 frames/second, respectively. They are all superior to other classic algorithms. It is evident that the proposed method can provide powerful support for badminton trajectory recognition and help improve the accuracy of badminton movement recognition.

A High‐Precision Dynamic Movement Recognition Algorithm Using Multimodal Biological Signals for Human–Machine Interaction

Accurate recognition of human dynamic movement is essential for seamless human–machine interaction (HMI) across various domains. However, most of the existing methods are single‐modal movement recognition, which has inherent limitations, such as limited feature representation and instability to noise, which will affect its practical performance. To address these limitations, this article proposes a novel fusion approach that can integrate two biological signals, including electromyography (EMG) and bioelectrical impedance (BI). The fusion method combines EMG for capturing dynamic movement features and BI for discerning key postures representing discrete points within dynamic movements. In this method, the identification of key postures and their temporal sequences provide a guiding framework for the selection and weighted correction of probability prediction matrices in EMG‐based dynamic recognition. To verify the effectiveness of the method, six dynamic upper limb movements and nine key postures are defined, and a Universal Robot that can follow movements is employed for experimental validation. Experimental results demonstrate that the recognition accuracy of the dynamic movement reaches 96.2%, representing an improvement of nearly 10% compared with single‐modal signal. This study illustrates the potential of multimodal fusion of EMG and BI in movement recognition, with broad prospects for application in HMI fields.

AI-enhanced backpack with double frequency-up conversion vibration energy converter for motion recognition and extended battery life

With growing interest in physical activities, wearable device sales have been increasing each year due to their help in tracking movement. However, keeping these devices running for a long time is still a key challenge. This paper presents an AI-enhanced backpack equipped with a hybrid vibration energy converter that integrates piezoelectric (PE) and electromagnetic (EM) technologies, featuring a double frequency-up conversion (FUC) mechanism. The PE unit enables efficient motion recognition, while the EM unit extends battery life via kinetic energy harvesting. The double FUC mechanism boosts human motion signal strength and resolution by converting sub-Hertz frequencies to hundreds of Hertz. This makes the device particularly effective in environments with performance constraints and interference. By adopting deep learning techniques, the system maintains a high motion recognition accuracy of 97.33 % even under low data sampling rates and significant noise interference. The EM unit effectively harvests kinetic energy, generating an average output power of 4.5 mW during activities such as running at 4.5 Hz, despite of the device’s compact size. Additionally, a power management circuit with human motion-stimulated switch optimizes battery life by managing the system’s sleep and active states. This innovative energy management approach ensures that during human activity, the system conserves energy by remaining in sleep state for at least 30 % of the time, significantly extending battery life and enhancing the backpack’s suitability for outdoor applications.

Finger Movement Recognition using Machine Learning Algorithms with Tree-Seed Algorithm

Finger Movement Recognition using Machine Learning Algorithms with Tree-Seed Algorithm

Multifunctional Nano‐Conductive Hydrogels With High Mechanical Strength, Toughness and Fatigue Resistance as Self‐Powered Wearable Sensors and Deep Learning‐Assisted Recognition System

AbstractHigh mechanical strength, toughness, and fatigue resistance are essential to improve the reliability of conductive hydrogels for self‐powered sensing. However, achieving mutually exclusive properties simultaneously remains challenging. Hence, a novel directed interlocking strategy based on topological network structure and mechanical training is proposed to construct tough hydrogels by optimizing the network structure and modulating the orientation of molecular chains. Combining Zn2+ crosslinked cellulose nanofibers (CNFs) and a polyacrylamide‐poly(vinyl alcohol) double‐network, the unique interlocked‐network structure exhibits an enhanced toughening effect due to hydrogen bonding and metal‐ligand interactions. The aligned nanocrystalline domains achieved by training further contribute to an increase in the toughness and fatigue thresholds. This innovative approach synergistically enhances the mechanical properties of the nano‐conductive hydrogel, achieving a maximum tensile strength of 4.98 MPa and a toughness of 48 MJ m−3. Notably, the CNFs template with anchored polyaniline, when oriented through mechanical training, forms a unique directional conductive pathway, which significantly enhances the power output performance. Besides, a motion recognition system based on a self‐powered sensing device is designed with the assistance of deep learning techniques to accurately identify human motion behaviors. This work showcases a potentially transformative flexible electronic material for self‐powered sensing systems and intelligent recognition systems.

A Highly Robust, Multifunctional, and Breathable Bicomponent Fibers Thermoelectric Fabric for Dual-Mode Sensing.

Wearable thermoelectric (TE) materials are seen as excellent candidates for flexible electronics because of their unique self-powered properties, multistimulus sensing and human waste heat conversion. However, currently reported flexible TE materials still face challenges such as poor durability, uncomfortable wearing and sensing signals crosstalking each other. Herein, this study describes a hot-air cross-linking method for the preparation of multifunctional TE fabrics with enhanced durability. Poly(ethylene terephthalate) (PET) fibers with core and sheath structures having different melting points were selected as flexible substrates. Poly(3,4-ethylenedioxythiophene)-poly(styrenesulfonate) (PEDOT:PSS) and single-walled carbon nanotubes (SWCNTs) were embedded stably on the surface of the sheath layer in the presence of heat treatment. The fiber-welded structure created by thermal cross-linking improves the durability of TE fabrics, including consistent mechanical and electrical properties after a 6 h wash test and 6000 compression cycles. The unique fiber structure of TE fabrics ensures excellent breathability (313.7 mm s-1 at 200 Pa), which meets the breathability requirements for human wear. In addition, the fiber-prepared sensors have excellent compressive strain response (20 ms response time and 30 ms recovery time) and precise temperature discrimination (0.17 K minimum discrimination temperature) for accurate real-time monitoring of the sensed signals. Thus, the TE fabrics can be used for human motion recognition, including pulse monitoring, sign language expression, and motions in joint areas. Moreover, the fabricated wearable TE device is connected to a Bluetooth module for wireless transmission, which can be used for mechanical and temperature sensing of the robot arm without signals crosstalking. This new durable TE fabric paves the way for the next generation of smart wearable technology.

Retraction Note: Design of optical tracking sensor based on image feature extraction for badminton athlete motion recognition

Retraction Note: Design of optical tracking sensor based on image feature extraction for badminton athlete motion recognition

Retraction Note: Application of infrared image detection based on high-resolution image processing in motion recognition

Retraction Note: Application of infrared image detection based on high-resolution image processing in motion recognition

Sports action recognition algorithm based on multi-modal data recognition

The recognition of sports action is an important research subject, which is conducive to the improvement of athletes’ own level. To improve the accuracy of multi-modal data action recognition, based on the Transformer module, this study introduces a multi-head attention mechanism, fuses multi-modal data, and constructs a multi-stream structured object relationship inference network. Based on PointNet++ network and combining five different data fusion frameworks, a motion recognition model that integrates RGB data and 3D skeleton point cloud is constructed. The results showed that the Top-1 accuracy of multi-stream structured object relationship inference network was 42.5% and 42.7%, respectively, which was better than other algorithms. The accuracy of the multi-modal fusion model was improved by 15.6% and 5.1% compared with the single mode, and by 5.4% and 2.6% compared with the dual mode, which showed its superiority in the action recognition task. This showed that the fusion of multi-modal data can provide more abundant information, so as to improve the accuracy of action recognition. The accuracy of the action recognition model combining RGB data and 3D skeleton point cloud was 84.3%, 87.5%, 90.2%, 90.6% and 91.2% after the combination of different strategies, which effectively compensated for the problem of missing information in 3D skeleton point cloud and significantly improved the accuracy of action recognition. With a small amount of data, the Top-1 accuracy of the multi-stream structured object relationship inference network in this study was superior to other algorithms, showing its advantages in dealing with complex action recognition tasks. In addition, the action recognition model that fuses RGB data and 3D skeleton point cloud also achieved higher accuracy, which is better than other algorithms. This study can meet the needs of motion recognition in different scenarios and has certain reference value.

Teachable Machine을 활용한 체육교과 인공지능 교수학습 모델 개발

Objectives The purpose of this study is to develop an artificial intelligence teaching and learning model for physical education that can be applied to actual physical education classes using teachable machines and to provide basic data that can be used in the field. Methods The rapid prototyping methodology was applied to carry out the process of analysis, design, development, implementation, and evaluation. Four physical education teachers, one coding expert, and four students were selected as research participants, and data were collected and analyzed through in-depth interviews. Results The prototype for developing the physical education and artificial intelligence teaching and learning model was developed through a four-round continuous feedback process, including developing a motion recognition program, adding voice functions, adding visual functions, and configuring a website considering ease of use. A teaching and learning model based on the areas of health, challenge, and competition was developed and presented. Conclusions The physical education and artificial intelligence teaching and learning model using teachable machines was developed and modified as a prototype through the rapid prototyping methodology, and was ultimately developed into 9 teaching and learning models in the areas of health, challenge, and competition.

A novel procedure to automate the removal of PLI and motion artifacts using mode decomposition to enhance pattern recognition of sEMG signals for myoelectric control of prosthesis

Hand Movement Recognition (HMR) with sEMG is crucial for artificial hand prostheses. HMR performance mostly depends on the feature information that is fed to the classifiers. However, sEMG often captures noise like power line interference (PLI) and motion artifacts. This may extract redundant and insignificant feature information, which can degrade HMR performance and increase computational complexity. This study aims to address these issues by proposing a novel procedure for automatically removing PLI and motion artifacts from experimental sEMG signals. This will make it possible to extract better features from the signal and improve the categorization of various hand movements. Empirical mode decomposition and energy entropy thresholding are utilized to select relevant mode components for artifact removal. Time domain features are then used to train classifiers (kNN, LDA, SVM) for hand movement categorization, achieving average accuracies of 92.36%, 93.63%, and 98.12%, respectively, across subjects. Additionally, muscle contraction efforts are classified into low, medium, and high categories using this technique. Validation is performed on data from ten subjects performing eight hand movement classes and three muscle contraction efforts with three surface electrode channels. Results indicate that the proposed preprocessing improves average accuracy by 9.55% with the SVM classifier, significantly reducing computational time.

An Integrated All-Natural Conductive Supramolecular Hydrogel Wearable Biosensor with Enhanced Biocompatibility and Antibacterial Properties.

Conductive hydrogels exhibit tremendous potential for wearable bioelectronics, biosensing, and health monitoring applications, yet concurrently enhancing their biocompatibility and antimicrobial properties remains a long-standing challenge. Herein, we report an all-natural conductive supramolecular hydrogel (GT5-DACD2-B) prepared via the Schiff base reaction between the biofriendly dialdehyde cyclodextrin and gelatin. The potent antibacterial agent fusidic acid (FA) is incorporated through host-guest inclusion, enabling 100% inhibition of Staphylococcus aureus proliferation. The biocompatibility of our hydrogel is bolstered with tannic acid (TA) facilitating antibacterial effects through interactions with gelatin, while borax augments conductivity. This supramolecular hydrogel not only exhibits stable conductivity and rapid response characteristics but also functions as a flexible sensor for monitoring human movement, facial expressions, and speech recognition. Innovatively integrating biocompatibility, antimicrobial activity, and conductivity into a single system, our work pioneers a paradigm for developing multifunctional biosensors with integrated antibacterial functionalities, paving the way for advanced wearable bioelectronics with enhanced safety and multifunctionality.

Flexible Near‐Infrared Organic Photodetectors With Ultralow Dark Current by Layer‐by‐Layer Blade Coating

AbstractNear‐infrared organic photodetector (NIR OPD) is promising for emerging wearable biosensing applications. However, their practical application is hindered by the high dark currents of devices that limit the detection of faint light. In this work, highly sensitive flexible NIR OPDs are presented with ultralow dark currents, fabricated using a layer‐by‐layer blade coating (LBL‐BC) technique. In the active layer, a fully fused‐ring molecule FM2, featuring a fixed molecular skeleton, is employed as an electron acceptor to reduce the trap density. The LBL‐BC method enhances the film order and phase purity of the active layer, significantly reduces the trap density of states, and optimizes the vertical phase separation structure of the thin film, thereby preventing reverse charge injection. As a result, a highly sensitive flexible NIR OPD is developed exhibiting an ultralow dark current density of 4.83 × 10−9 A cm−2 at −0.3 V bias and an extremely low noise current density of 7.65 × 10−15 A Hz−1/2 at 10 Hz, comparable to commercial silicon photodiodes. Furthermore, this flexible device is successfully applied for real‐time monitoring of human heartbeat rate, oxygen saturation, and motion recognition. These findings advance the development of highly sensitive NIR OPDs and their application in wearable biosensing technologies.

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.

AI-Assisted Self-Powered Vehicle-Road Integrated Electronics for Intelligent Transportation Collaborative Perception.

Collaborative perception between a vehicle and the road has the potential to enhance the limited perception capability of autonomous driving technologies. With this background, self-powered vehicle-road integrated electronics (SVRIE) with a multilevel fractal structure is designed to play a dual role, including a SVRIE device integrated into vehicle tires and a SVRIE array embedded into a road surface. The pressure sensing capability and anti-crosstalk performance of the SVRIE array are characterized separately to validate the feasibility of applying the SVRIE in a cooperative vehicle-infrastructure system. It is demonstrated that the SVRIE based on the multi-layered fractal structure exhibits maximum performance in collaborative sensing and interaction between vehicles and road information, such as vehicle motion, road surface condition, and tire life cycle health monitoring. Traditional data analysis methods are often of questionable accuracy. Therefore, a convolutional neural network is used to classify the vehicle and road conditions with accuracy of at least 88.3%. The transfer learning model is constructed to enhance the road surface identification capabilities with 100% accuracy. The accuracies of the vehicle tire motion recognition and tire health monitoring are 97% and 99%, respectively. This work provides new ideas for collaborative perception between vehicles and roadsides.

Quantitative assessment of human motion for health and rehabilitation: A novel fuzzy comprehensive evaluation approach

In the pursuit of advancing health and rehabilitation, the quintessence of human motion recognition technology has been underscored through its quantitative contributions to physical performance assessment. This research delineates the inception of a novel fuzzy comprehensive evaluation-based recognition method that stands at the forefront of such innovative endeavours. By synergistically fusing multi-sensor data and advanced classification algorithms, the proposed system offers a granular quantitative analysis with implications for health and fitness monitoring, particularly rehabilitation processes. Our methodological approach, grounded in the modal separation technique and Empirical Mode Decomposition (EMD), effectively distills the motion acceleration component from raw accelerometer data, facilitating the extraction of intricate motion patterns. Quantitative analysis revealed that our integrated framework significantly amplifies the accuracy of motion recognition, achieving an overall recognition rate of 90.03 %, markedly surpassing conventional methods, such as Support Vector Machines (SVM), Decision Trees (DT), and K-Nearest Neighbors (KNN), which hovered around 80 %. Moreover, the system demonstrated an unprecedented accuracy of 97 % in discerning minor left-right swaying motions, showcasing its robustness in evaluating subtle movement nuances—a paramount feature for rehabilitation and patient monitoring. This marked precision in motion recognition heralds a new paradigm in health assessment, enabling objective and scalable analysis pertinent to individualized therapeutic interventions. The experimental evaluation accentuates the system's adeptness at navigating the dichotomy between complex, intense motions and finer, subtler movements with a high fidelity rate. It substantiates the method's utility in delivering sophisticated, data-driven insights for rehabilitation trajectory monitoring.

Body temperature-triggered adhesive ionic conductive hydrogels for bioelectrical signal monitoring

Bioadhesive ion-conductive hydrogels are essential in bioelectrical signal monitoring. However, highly adhesive hydrogels may cause discomfort, such as skin redness and pain when peeled off, which hinders their application. Here, we design a body temperature-triggered adhesive ionic conductive hydrogel based on biocompatible polyacrylamide (PAAM), gelatin, and sodium alginate (SA). Due to the temperature sensitivity of gelatin, the adhesion of the developed PAAM/gelatin/SA (PGS) hydrogel is rapidly enhanced upon exposure to human body temperature, and it decreases quickly within a short period (10 s) after cooling, thus enabling painless removal. At the same time, the hydrogel exhibits a Young’s modulus (∼265 kPa) similar to that of skin tissue and achieves a conformal contact to the skin. Benefiting from the low electrode-epidermal impedance, the developed hydrogel has a decent signal-to-noise ratio of about 25 dB, allowing high-fidelity recording of various bioelectrical signals and accurate recognition of movements. The temperature-triggered adhesion-changing conductive hydrogel presented in this work shows great potential for biomedical applications.

Research on Artificial Intelligence Technology in Accurate Recognition of Sports Training Actions

In sports training, accurate identification of athletes' movements is helpful to judge whether athletes' actions are standard or not, thus providing precise movement data for training and improving athletes' levels. The convolutional neural network VGG model was constructed based on artificial intelligence technology to extract image features and accurately recognize actions automatically. Weizmann data set was used to train and verify the model. The results of this study showed that the motion recognition algorithm based on the convolutional neural network could achieve high recognition accuracy.