Motion Recognition Technology Research Articles

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.

Wushu Movement Recognition System Based on DTW Attitude Matching Algorithm

Motion recognition technology is widely used in intelligent video surveillance, human-computer interaction and other fields. With the development of computer vision technology, improving the accuracy and efficiency of motion recognition has become the focus of research. The purpose of this study is to improve the performance of Wushu movement recognition through improved dynamic time warping algorithm and hierarchical model. Firstly, a high-dimensional feature vector is constructed by using the position, velocity and Angle changes of human bone joints. The actions are subdivided by the hierarchical model, and matched and recognized by the max-minimum dynamic time regularization model. Meanwhile, the K-class mean algorithm is combined to optimize the type of tree core, improve the performance of the model, reduce the interference of noise nodes, and effectively classify Wushu actions. Experimental verification was carried out on four public data sets of KTH, Olympic Sports, Hollywood2 and HMDB51. The experimental results showed that the recognition rate of the proposed model in KTH data set was 95.2%, and that in Olympic Sports data set was 91.4%. The Hollywood2 dataset was 66.7%, and the HMDB51 dataset was 61.2%. Comparing the results of different algorithms, the proposed method improved the recognition performance by 10% compared with long short-term memory network and gated cycle unit. Compared with one-dimensional convolutional neural network, the time of the proposed method was 15s longer, but the recognition rate was 1.6% higher. The results showed that the proposed method had significant performance advantages in diverse and complex action recognition tasks. Meanwhile, the results emphasized the factors to be considered in the design of the model, demonstrating its effectiveness in the application of Wushu movement recognition.

The Role of the Combination of 3D Simulation Sequence Diagram and Video Motion Recognition Technology in Evaluating and Correcting Dancers' Dance Moves

The Role of the Combination of 3D Simulation Sequence Diagram and Video Motion Recognition Technology in Evaluating and Correcting Dancers' Dance Moves

Efficient human activity recognition: A deep convolutional transformer-based contrastive self-supervised approach using wearable sensors

Artificial intelligence has advanced the applications of sensor-based human motion capture and recognition technology in various engineering fields, such as human–robot collaboration and health monitoring. Deep learning methods can achieve satisfactory recognition results when provided with sufficient labeled data. However, labeling a large dataset is expensive and time-consuming in practical applications. To address this issue, this paper proposes a deep convolutional transformer-based contrastive self-supervised (DCTCSS) model under the bootstrap your own latent (BYOL) framework. The DCTCSS model aims to achieve reliable activity recognition using only a small amount of labeled data. Firstly, a deep convolutional transformer (DCT) model is proposed as the backbone of DCTCSS model, to learn high-level feature representations from unlabeled data in pre-training period. Subsequently, a simple linear classifier is trained with supervised fine-tuning using a limited amount of labeled data to recognize activities. In addition, this paper experimentally formulates a random data augmentation strategy to increase the diversity of input data. The performance of the DCTCSS model is evaluated and compared with several state-of-the-art algorithms on three datasets widely used in daily life, medical monitoring, and intelligent manufacturing. Experimental results show that the DCTCSS model achieves mean F1 scores of 95.64%, 88.39%, and 98.40% on the UCI-HAR, Skoda, and Mhealth datasets, respectively, using only 10% of the labeled data. These results outperform both supervised and unsupervised baseline models. Consequently, the DCTCSS model demonstrates its effectiveness in reducing the dependence on large amounts of labeled data while still achieving competitive activity recognition performance.

FCTNet: Fusion of 3D CNN and transformer dance action recognition network

Folk dance is an important intangible cultural heritage in China. In the environment where movement recognition technology is widely used, there is still no research field on the protection and inheritance of folk dance culture. In order to better protect and inherit the minority dance, screening the typical movements of 5 types of minority dance, through the dance video frame processing, obtain the key movements of 19 class dance sequence, build the national dance typical action data set, put forward a 3D CNN fusion Transformer national dance recognition network model (FCTNet), the recognition rate of 96.7% in the experiment. The results show that the construction method of the folk dance data set is reasonable, the identification model has good performance for the classification of folk dance, and can effectively identify and record the folk dance movements, which also makes new contributions to the digital protection of folk dance.

Retraction Notice: Aided Image Acquisition System for Aerobics Training Based on Motion Recognition Technology

Retraction Notice: Aided Image Acquisition System for Aerobics Training Based on Motion Recognition Technology

Application of 3D recognition algorithm based on spatio-temporal graph convolutional network in basketball pose estimation

In recent years, human motion recognition in computer vision has become a hot research direction in this field. Based on 2D human motion recognition technology, real-time extraction of motion features from 2D planes is used to recognize human movements. This method can only learn the position contour and color information of the image. It cannot directly reflect the motion situation, which results in low recognition accuracy and efficiency. In response to this issue, this study proposes a combination method of motion recognition and 3D pose estimation to recognize and classify basketball movements. First, the 2D skeleton model is obtained by extracting the feature information in the video action, which is converted into a 3D model, and the model is replaced by the time-space convolutional network to establish a human action recognition model. The experiment showed that when the number of iterations reached 6, the accuracy of the spatio-temporal graph convolutional network algorithm model reached 92%. Comparing the accuracy rates of different algorithm models, the average accuracy rates of convolutional neural network, long short memory network, graph convolution, long short model of action recognition and graph convolution model of action recognition were 61.6%, 65.4%, 72.5%, 76.8% and 90.3% respectively. The results show that the proposed 3D recognition algorithm can accurately recognize different basketball movements. This study can provide reference for basketball coaches and athletes in basketball training.

A Study on the Innovation of Traditional Drama Performance Forms by Intelligent Media Based on AI-Assisted Analysis Framework

Abstract This paper investigates the impact of contemporary entertainment preferences among youth, who increasingly favor novel forms over traditional theater arts, driven by the rapid pace of modern life. Specifically, it explores how action recognition technology can transform theatrical performance forms through the lens of intelligent media. Utilizing the inertial sensor MPU9250, the study mathematically captures the action characteristics of theatrical performances using quaternion algebra. This process involves preprocessing steps that set the stage for coordinate system transformations. Furthermore, the research constructs a database of theatrical morphology by classifying and storing morphological features derived from a human 3D skeleton dataset. The effectiveness of motion recognition technology in enhancing theatrical performances is then empirically tested and analyzed. The results reveal that this technology successfully identifies morphological movements in traditional theater with accuracies of 74.42%, 73.77%, and 64.45%, respectively. An additional pedagogical assessment highlights the significant role of motion recognition technology in educational contexts. In particular, the study notes a substantial difference in the ‘imagination’ dimension scores between control and experimental groups. The control group scored an average of 6.67, whereas the experimental group achieved 8.07, indicating enhanced engagement and cognitive stimulation through the application of motion recognition. These findings underscore the potential of such technology to offer theoretical and practical insights for the revitalization and progressive development of traditional theatrical performance forms.

Research on the Design and Application of Intelligent Teaching and Training System for College Basketball Based on 3D Motion Recognition Technology

Abstract In this paper, for the problems of traditional 2D convolutional networks in dealing with action recognition in video, a dual-resolution 3D-CNN action recognition network is induced to initialize 3D convolutional weight parameters by using 2D weight parameters of ImageNet, and the weight files are used as the parameters of the model to perform feature extraction on the sequence of technical action frames respectively. After feature extraction, collect the feature vectors that can effectively describe the human body movements, classify the feature vectors with the help of the kernel function in the Support Vector Machine (SVM), and finally determine the technical architecture of the basketball auxiliary training system, and complete the design of the intelligent teaching and training system for basketball in colleges and universities. After analyzing the method of the present paper, it can be seen that the recognition error of the basketball player’s running gait is 3.47%, which accurately reflects basketball sports training footwork trajectory to achieve the effect of intelligent guidance. In addition, the upper and lower limb movement recognition effect based on CNN+SVM is obvious, and the average accuracy rate of its upper and lower limb movements is 93.17% and 98.66%, respectively, which well meets the needs of basketball teaching in colleges and universities. In terms of students’ basketball skills, there is a significant difference between this paper’s system and traditional teaching (P<0.05), and this paper’s system has a higher priority for improving the quality of college basketball teaching than traditional teaching.

Development and Usability Evaluation of Surgical Sterilization Content Using Motion Recognition Technology

Development and Usability Evaluation of Surgical Sterilization Content Using Motion Recognition Technology

Research on the Application of Dance Talent Cultivation Mode in Colleges and Universities in the Context of Multimedia Era

Abstract This paper combines Kinect and convolutional neural networks to construct a dance movement recognition technology based on 3D CNNs. Applying dance movement recognition technology to dance teaching builds a new mode of training dance talents in colleges and universities. The role of Kinect in recognizing dance movements is explored from three aspects: real-time motion capture, human skeleton tracking, and information input. The dance movement image is computerized by calculating the depth of the points using stereo analysis. To analyze dance movements and classify them by features, a convolutional neural network is combined. Based on two-dimensional convolutional neural networks, three-dimensional convolutional neural networks have been constructed, which improve the comprehensiveness of dance movement information. By combining dance movement recognition technology with dance talent cultivation, we analyze the students’ professional dance ability and the teaching effect under the new talent cultivation mode. The results show that the teaching effect of the dance talent cultivation mode combined with the movement recognition technology is better, and the percentage of students dance movements reaching the standard in a movement completion is 0.95. The professional ability of dance talent is 0.8 percent.

Application of video image processing in sports action recognition based on particle swarm optimization algorithm

The existing sports training methods are mainly aimed at the sports field environment. The traditional sports training is only based on the coaches' visual inspection and combined with their own experience to put forward suggestions, which is relatively inefficient, thus limiting the rise of athletes' sports training level to a certain extent. Based on this background, combining traditional physical education teaching methods with video image processing technology, especially using particle swarm optimization algorithm, can promote the application of human motion recognition technology in physical training. This paper mainly investigates the optimization process of particle swarm optimization algorithm and discusses the development of particle swarm optimization algorithm; Secondly, through video decoding, image noise removal, video enhancement and other methods, complete video image processing and establish the structure of the manikin to achieve the collection of key points of the target, and then collect relevant data with experimental methods The results show that the motion recognition system proposed in this paper can effectively detect the changes of athletes' sampling point path, and can be compared with standard movements, which has a good auxiliary role. With the application of video image processing technology in sports training becoming more and more common, athletes can analyze their training videos in a more intuitive way and find out shortcomings, so as to improve the training effect. This paper studies particle swarm optimization algorithm and applies it to the field of video image processing, which promotes the development of sports action recognition technology based on video processing.

Dance Video Motion Recognition Based on Computer Vision and Image Processing

ABSTRACT In recent years, motion recognition has become a hot research topic in the field of computer vision and has great research worth. Scholars and research institutions at home and abroad have made a lot of research and achieved good results. However, the research results of applying motion recognition technology to dance video are relatively few, mainly because the complexity of human motion in dance video is relatively high. In article, an optimized design scheme for motion recognition research of dance video based on computer vision and image processing is proposed. The collected dance video is preprocessed, including grayscale, background subtraction and filter denoising, so as to extract the character features in the video image. Then, the method of self-organizing mapping neural network (SOM) in computer vision is used to realize the recognition of dance movements. Finally, the simulation test and analysis are carried out. The simulation results show that the proposed arithmetic has a certain accuracy, which is 9.34% higher than the traditional arithmetic. The research of motion recognition method based on dance video will also play a certain reference role for the research of human motion recognition in a large number of real and complex environments, and enrich the application field of motion recognition technology.

Image recognition of sports dance teaching and auxiliary function data verification based on neural network algorithm

Nowadays, physical dance is widely spread in the society as an emerging sport. Dance movement is favored by people because of its unique social function and fitness effect. For dance teaching, dance movement analysis can help optimize and improve the existing dance movements and the understanding and inheritance of traditional dance movements. With the rise of online teaching, intelligent identification and analysis of dance movements can promote the better development of sports dance teaching. However, the relevant research in this area is still very scarce. As the basis of this kind of research, there is an urgent need for dance motion recognition technology. Based on this background, this paper by introducing neural network algorithm for dance teaching sports image special recognition design, the algorithm can combine feature extraction technology to process video, extract the dance movements in the target data set, then for the extraction of cumulative feature extraction operation, in order to accumulate all the collected target features, so as to further complete the gradient histogram acquisition. Through the design experimental test, the cumulative feature image extraction results obtained through the algorithm are obviously better than the traditional image recognition results, so the design rationality and effectiveness of the algorithm are proved, and the sports dance teaching can be specially assisted. This paper designs an effective auxiliary image recognition algorithm by introducing the neural network algorithm into the field of sports dance teaching.

Location Adaptive Motion Recognition Based on Wi-Fi Feature Enhancement

Action recognition is essential in security monitoring, home care, and behavior analysis. Traditional solutions usually leverage particular devices, such as smart watches, infrared/visible cameras, etc. These methods may narrow the application areas due to the risk of privacy leakage, high equipment cost, and over/under-exposure. Using wireless signals for motion recognition can effectively avoid the above problems. However, the motion recognition technology based on Wi-Fi signals currently has some defects, such as low resolution caused by narrow signal bandwidth, poor environmental adaptability caused by the multi-path effect, etc., which make it hard for commercial applications. To solve the above problems, we first propose and implement a position adaptive motion recognition method based on Wi-Fi feature enhancement, which is composed of an enhanced Wi-Fi features module and an enhanced convolution Transformer network. Meanwhile, we improve the generalization ability in the signal processing stage to avoid building an extremely complex model and reduce the demand for system hardware. To verify the generalization of the method, we implement real-world experiments using 9300 network cards and the PicoScenes software platform for data acquisition and processing. By contrast with the baseline method using original channel state information(CSI) data, the average accuracy of our algorithm is improved by 14% in different positions and over 16% in different orientations. Meanwhile, our method has best performance with an accuracy of 90.33% compared with the existing models on public datasets WiAR and WiDAR.

Machine Learning-Based Aerobic Exercise Recognition and Its Effect on Health Status of College Students

It was to explore the role of motion recognition technology based on the convolutional neural network (CNN) model in guiding the aerobic exercise of college students and its impact on their physical and mental health, providing a scientific and effective method for students to have a healthy body and mood. 256 college students were randomly divided into control group (appropriate aerobic exercise was selected by self-evaluation, and exercise was performed spontaneously) and observation group (aerobic exercise under the guidance of machine learning model-based motion recognition technology), 128 cases in each group. The changes in the physical and psychological status of the two groups of college students were evaluated after one year. The recognition rate of CNN algorithm test results (92.98%) and the recognition accuracy of stationary, walking, running, squatting, hand raising, seated position, lunge, and other movement states were higher than those of recurrent neural network (RNN) (52.22%) and deep neural network (DNN) (40.21%) algorithms ( P < 0.05 ); after 1 year of exercise, the 800/1,000 m running performance, standing long jump performance, 1 min sit-up performance, vital capacity, maximal ventilator volume per minute (MVV) and vital capacity/body mass index, stroke volume (SV), cardiac output (CO), ejection fraction (EF), heart rate (HR), ejection time (ET), mean systolic ejection rate (MSER), mean velocity of circumferential fibre shortening (MVCF) of the observation group were superior to those of the control group, and tension, anger, depression, fatigue, panic, energy, self-related emotions, and the improvement of total mood disturbance (TMD) score were also superior to those before exercise. The improvement effect of physical fitness, cardiopulmonary function, and mentality of college students in the observation group was better than that in the control group ( P < 0.05 ). Aerobic exercise can effectively improve the physical fitness, cardiopulmonary function, and adverse mentality of college students, and through the assistance of machine learning, exercise recognition technology can further improve the effect of aerobic exercise, which is worthy of application and promotion.

P‐2.4: Development and Prospect of Medical Action Recognition Technology forVirtual Reality (VR)

P‐2.4: Development and Prospect of Medical Action Recognition Technology forVirtual Reality (VR)

A Table Tennis Motion Correction System Based on Human Motion Feature Recognition

Computer-aided sports systems are an important area of current research. To be specific, it can combine computer vision, computer graphics, and motion capture technologies with the characteristics of sports, and design and develop assisted systems according to user needs. As a result, it is of great practical importance and application to improve the entertainment and science of sports learning, to stimulate public participation in sports, and to learn sports skills. Table tennis has a very great popular base in China and has a wide range of audiences. However, for beginners or amateurs of table tennis, it is difficult to learn table tennis skills by relying on books, videos, etc., due to the lack of professional technical guidance, which makes it relatively difficult for them to continuously improve their skills. Sometimes they tend to form wrong technical habits, which are not easy to detect and correct in time, and can lead to sports injuries over time. Therefore, the lack of professional instruction has hindered the development of table tennis in public fitness to a certain extent. On the other hand, in a table tennis course, the learning of table tennis techniques is mostly dependent on the teacher’s explanation. Students are bored with the repetitive and boring teaching because they need to repeat the exercises several times. The formation of correct table tennis movements requires repeated instruction from the instructor to address problems with the student’s movements. However, due to limited teaching resources, it is difficult for teachers to accommodate different students during the teaching process. In addition, with a limited team of instructors, the level of proficiency varies, making it difficult for them to make an objective assessment. As a result, with the rapid development and application of modern science and technology in the field of sports, there is an urgent need to explore intelligent sports technology instructional support systems to improve the shortcomings of the traditional teaching model. Motion recognition technology, as an essential branch of multimodal interaction technology, has been developing rapidly in recent years. This research focus on designing and implementing a system for recognizing human motion based on skeletal point pose information by combining inertial sensors. This system can collect data from sensors located at the main skeletal points of the human body and transmits them to the host computer through multi-Bluetooth pairing transmission. After that, support vector machines are applied to classify human movements and to recognize general human movements. This system has significant advantages for human movement recognition and classification due to the distinctive technical characteristics of table tennis. The recognition and classification of both players in the video can play an important role in the technical analysis and tactical arrangement of the players. As a result, the table tennis motion correction system based on human motion feature recognition has crucial research significance and application value.

A Deep Learning Method for Intelligent Analysis of Sports Training Postures.

With the further research of artificial intelligence technology, motion recognition technology is widely used in posture analysis of sports training. However, the interference of light, Angle, and distance in real life makes the existing model unable to focus on the expression of human movements. Aiming at the above problems, this paper proposes a motion training attitude analysis method based on a multiscale spatiotemporal graph convolution network. Firstly, the spatiotemporal image of the skeleton is constructed, and then the convolution operation is performed on the spatiotemporal image of the skeleton. Finally, the convolution results are linearly weighted and fused to capture the characteristics of action types with different time lengths. At the same time, the algorithm increases the processing of some important information loss and increases the randomness of the data set. Experimental results show that the proposed algorithm can adapt to the behavior changes of different complexity, and the model performance and recognition accuracy are significantly improved.

Optimization of Choreography Teaching with Deep Learning and Neural Networks.

To improve the development level of intelligent dance education and choreography network technology, the research mainly focuses on the automatic formation system of continuous choreography by using the deep learning method. Firstly, it overcomes the technical difficulty that the dynamic segmentation and process segmentation of the automatic generation architecture in traditional choreography cannot achieve global optimization. Secondly, it is an automatic generation architecture for end-to-end continuous dance notation with access to temporal classifiers. Based on this, a dynamic time-stamping model is designed for frame clustering. Finally, it is concluded through experiments that the model successfully achieves high-performance movement time-stamping. And combined with continuous motion recognition technology, it realizes the refined production of continuous choreography with global motion recognition and then marks motion duration. This research effectively realizes the efficient and refined production of digital continuous choreography, provides advanced technical means for choreography education, and provides useful experience for school network choreography education.