Motion Recognition Technology Research Articles

Improved Graph Convolutional Neural Network for Dance Tracking and Pose Estimation.

Movement recognition technology is widely used in various practical application scenarios, but there are few researches on dance movement recognition at present. Aiming at the problem of low accuracy of dance movement recognition due to complex pose changes in dance movements, this paper designed an improved graph convolutional neural network algorithm for dance tracking and pose estimation. In this method, the spatial and temporal characteristics of motion are extracted from the skeleton joint diagram of human body. Then, GCN (graph convolutional neural) is used to extract potential spatial information between skeleton nodes. Finally, LSTM (long short-term memory) extracts the time series features before and after human actions as a supplement and performs late fusion of the prediction outputs of the two networks, respectively, to improve the problem of insufficient generalization ability of single network. The experimental results show that this method can effectively improve the accuracy of dance movement recognition in general movement recognition data set and dance pose data set. It has certain application value in dance self-help teaching, professional dancer movement correction, and other application scenarios.

The Design of the Lightweight Smart Home System and Interaction Experience of Products for Middle-Aged and Elderly Users in Smart Cities.

The research aims to improve the comfort and safety of the smart home by adding a motion recognition algorithm to the smart home system. First, the research status of motion recognition is introduced. Second, based on the requirements of the smart home system, a smart home system is designed for middle-aged and elderly users. The software system in this system includes intelligent control subsystems, intelligent monitoring subsystems, and intelligent protection subsystems. Finally, to increase the security of the smart home, the intelligent monitoring subsystem is improved, and an intelligent security subsystem is proposed based on a small-scale motion detection algorithm. The system uses three three-dimensional (3D) convolutional neural networks (CNNs) to extract three image features, so that the data information in the video can be fully extracted. The performance of the proposed intelligent security subsystem based on a small-scale motion detection algorithm is compared and analyzed. The research results show that the accuracy of the system on the University of Central Florida (UCF101) dataset is 94.64%, and the accuracy on the HMDB51 dataset is 90.11%, which is similar to other advanced algorithms. Observing whether there are dangers such as falling inside and outside the family through motion recognition technology has very important application significance for protecting people's personal safety, life, and health.

Analysis of Basketball Technical Movements Based on Human-Computer Interaction with Deep Learning.

With the continuous development of computer technology, analysis techniques based on various types of sports data sets are also evolving. One typical representative is image-based motion recognition technology, which enables video action recognition with a certain degree of feasibility. In basketball technical action videos, technical action has obvious characteristics. The athletes in the footage in sports videos are relatively fixed, and the scenes are relatively homogeneous, so technical action analysis of basketball technical action videos has certain advantages. However, there are many challenges in basketball technical action recognition, mainly including the fact that basketball techniques are numerous and complex. To address the above issues, this research proposes a 3D convolutional neural network framework that two different resolution image inputs are performed on the basketball technical action dataset. The experimental results show that the algorithmic process designed in this study is effective for action recognition on the basketball technical action dataset.

Swimmer’s Posture Recognition and Correction Method Based on Embedded Depth Image Skeleton Tracking

With the continuous emergence of depth image recognition technology, human motion recognition technology has gradually come to life. However, because the current technology of image recognition and skeletal tracking is too backward, people cannot use gesture recognition robots to perform gesture recognition and correction with high accuracy for athletes. In this study, the skeletal tracking depth image is obtained through the Kinect sensor. When using the Kinect sensor to acquire images, the requirements for the environment around the measured object are very low, and it will not be affected by conditions such as light, shadow, and object occlusion, and the pose can be segmented in real time in a complex background. In this study, the depth map feature and the bone information feature are selected for fusion. The HOD feature is difficult to deal with the occlusion problem, and it is not easy to detect the excessive range of the human body gesture or the change of the object direction. Based on this, the HOD feature is improved in many places to form a new 3D-HOD feature and DMM-HOG feature. In this study, the technical research on posture recognition and correction of swimmers in the experiment has realized posture collection, posture segmentation, posture analysis, posture modeling, and posture recognition. In this study, random forests are combined with HOD features to classify pixel-by-pixel points in depth images. The accuracy of training pixel classification is as high as 85%, and the average recognition time of the decision tree for each pose is about 9% higher than that of random forests. The use of Meanshift algorithm to cluster the classified pixels to form skeletal joint points is fast and efficient, and can quickly and accurately find joint points. It has also achieved good experimental effects on human motion recognition and continuous motion recognition, is more suitable for real life, and has commercial practical value.

Dance Movement Recognition Based on Multimodal Environmental Monitoring Data.

Fine motion recognition is a challenging topic in computer vision, and it has been a trendy research direction in recent years. This study combines motion recognition technology with dance movements and the problems such as the high complexity of dance movements and fully considers the human body's self-occlusion. The excellent motion recognition content in the dance field was studied and analyzed. A compelling feature extraction method was proposed for the dance video dataset, segmented video, and accumulated edge feature operation. By extracting directional gradient histogram features, a set of directional gradient histogram feature vectors is used to characterize the shape features of the dance video movements. A dance movement recognition method is adopted based on the fusion direction gradient histogram feature, optical flow direction histogram feature, and audio signature feature. Three components are combined for dance movement recognition by a multicore learning method. Experimental results show that the cumulative edge feature algorithm proposed in this study outperforms traditional models in the recognition results of HOG features extracted from images. After adding edge features, the description of the dance movement shape is more effective. The algorithm can guarantee a specific recognition rate of complex dance movements. The results also verify the effectiveness of the movement recognition algorithm in this study for dance movement recognition.

Accuracy Verification of 3D Motion Analysis System Using Smart-phone Monocular Camera

PURPOSE This study aimed to verify the accuracy of three-dimensional (3D) motion data produced through artificial intelligence-based user motion recognition technology with images obtained using a smartphone monocular camera. This was done to explore the possibility of developing an application that can improve the reliability of the measurement of physical activity performing motions and feedback provision.METHODS To check the accuracy of the artificial intelligence-based 3D motion analysis system that utilized a semi-supervised learning method, a commercialized 3D infrared motion analysis system measured and compared motions on three movement planes, motions with limited joint movement, and fast motions in a wide moving range.RESULTS The motions on the coronal and sagittal planes produced through the 3D motion analysis application showed very high measurement accuracy; however, the accuracy of the measurement of motions on the horizontal plane, which could not be measured directly with a camera, was relatively lower than that of the coronal and sagittal planes. Accuracy in measuring 3D motion was moderate in moving motions and low in motions with limited joint movement.CONCLUSIONS For the developed 3D motion analysis system to be used in online physical education, the types of physical activities included in the program should be comprehensively composed through the analysis of the content system of the physical education curriculum and the resultant physical activities.

Human Motion Tracking Algorithm Based on Image Segmentation Algorithm and Kinect Depth Information

Human motion recognition has an important application value in scenarios such as intelligent monitoring and advanced human-computer interaction, and it is an important research direction in the field of computer vision. Traditional human motion recognition algorithms based on two-dimensional cameras are susceptible to changes in light intensity and texture. The advent of depth sensors, especially the Kinect series with good performance and low price released by Microsoft, enables extensive research based on depth information. However, to a large extent, the depth information has not overcome these problems based on two-dimensional images. This article introduces the research background and significance of human motion recognition technology based on depth information, introduces in detail the research methods of human motion recognition algorithms based on depth information at home and abroad, and analyzes their advantages and disadvantages. The public dataset is introduced. Then, based on the depth information, a method of human motion recognition is proposed and optimized. A moving human body image segmentation method based on an improved two-dimensional Otsu method is proposed to solve the problem of inaccurate and slow segmentation of moving human body images using the two-dimensional Otsu method. In the process of constructing the threshold recognition function, this algorithm not only uses the cohesion of the pixels within the class but also considers the maximum variance between the target class and the background class. Then, the quantum particle swarm algorithm is used to find the optimal threshold solution of the threshold recognition function. Finally, the optimal solution is used to achieve accurate and fast image segmentation, which increases the accuracy of human body motion tracking by more than 30%.

All-in-one two-dimensional retinomorphic hardware device for motion detection and recognition.

With the advent of the Internet of Things era, the detection and recognition of moving objects is becoming increasingly important1. The current motion detection and recognition (MDR) technology based on the complementary metal oxide semiconductor (CMOS) image sensors (CIS) platform contains redundant sensing, transmission conversion, processing and memory modules, rendering the existing systems bulky and inefficient in comparison to the human retina. Until now, non-memory capable vision sensors have only been used for static targets, rather than MDR. Here, we present a retina-inspired two-dimensional (2D) heterostructure based retinomorphic hardware device with all-in-one perception, memory and computing capabilities for the detection and recognition of moving trolleys. The proposed 2D retinomorphic device senses an optical stimulus to generate progressively tuneable positive/negative photoresponses and memorizes it, combined with interframe differencing computations, to achieve 100% separation detection of moving trichromatic trolleys without ghosting. The detected motion images are fed into a conductance mapped neural network to achieve fast trolley recognition in as few as four training epochs at 10% noise level, outperforming previous results from similar customized datasets. The prototype demonstration of a 2D retinomorphic device with integrated perceptual memory and computation provides the possibility of building compact, efficient MDR hardware.

Research on Sports Training Action Recognition Based on Deep Learning

With the rapid development of science and technology in today’s society, various industries are pursuing information digitization and intelligence, and pattern recognition and computer vision are also constantly carrying out technological innovation. Computer vision is to let computers, cameras, and other machines receive information like human beings, analyze and process their semantic information, and make coping strategies. As an important research direction in the field of computer vision, human motion recognition has new solutions with the gradual rise of deep learning. Human motion recognition technology has a high market value, and it has broad application prospects in the fields of intelligent monitoring, motion analysis, human-computer interaction, and medical monitoring. This paper mainly studies the recognition of sports training action based on deep learning algorithm. Experimental work has been carried out in order to show the validity of the proposed research.

Motion recognition technology of badminton players in sports video images

Intelligent identification technology has made great progress in transportation, and its application in sports has attracted widespread attention. This research mainly discusses the research of human motion recognition technology in sports dance video images. In geometric algebraic space, using instance templates, a new cfrdF method, using css-based similarity of human body features as features, to construct an angle-adaptive and continuous-scale space template matching algorithm to calculate the similarity between horizontal plates and detected images value, set a certain threshold, so as to match the area where the human body is located. Based on the actual video analysis and display, using the self-similar structure of the color of the human object as the basic feature, it is described by the geometric algebra method, and the human body in the video image is extracted using template convolution objects and design adaptive template functions to extract human movements from sports dance video images. By loading each test video in turn, the posture of the characters in the video is checked every 30 s, and the approximate positions of the head and feet are marked and output to a txt file. Because lying on the ground during sports dance is a way of matching by height correction, the accuracy is less disturbed, but it can still reach 90.9%. The research results show that the template matching robot model proposed in this paper can accurately and robustly extract human objects in video.

Research on Human Action Recognition Algorithm Based on Video Image

Human Action recognition is one of the research hotspot in the field of computer vision and artificial intelligence. It can usually be applied in many fields such as virtual reality, intelligent pension, medical monitoring, child monitoring, public safety and business management. Due to the complexity and variability of human movements, it is difficult and challenging to use computers to accurately identify actions. Therefore, it is of great significance to study the algorithm of motion recognition for video images. In the study of motion recognition algorithm, RGB data are used as input, and Slowfast Network and I3D algorithm models are used for training respectively. Experimental results show that the training accuracy of SlowFast algorithm is 96.66%. The training accuracy of I3D algorithm is as high as 99.16%, and both algorithms can accurately identify human movements. This study confirms that the human motion recognition technology and algorithm will have a wide range of application scenarios in the future. After solving the technical problems of low recognition rate, the recognition algorithm and technology will be widely promoted and applied in various fields.

The Accurate Repair of Image Contour of Human Motion Tracking Based on Improved Snake Model

With the rapid development of sports science, human motion recognition technology, as a new biometric recognition technology, has many advantages, such as noncontact target, long recognition distance, secret recognition process, and so on. Traditional human motion recognition technology is affected by environmental factors such as motion background, which is prone to rough edges of the recognized objects and loss of motion tracking information, thus further reducing the recognition accuracy. In this paper, the traditional snake model will be improved and optimized to improve the defect of human motion model contour extraction, so as to realize the accurate repair of image contour; in terms of algorithm running time, this paper innovatively improves the construction process of the snake model, further improves the running time of model evaluation, and solves the concave contour problem of corresponding moving objects in the snake model. In order to solve the problem of accurate convergence, this paper improves the snake model of the average moving algorithm and sets the corresponding weight coefficient to distinguish the corresponding moving target background, so as to achieve the convergence of the differential concave contour. In order to verify the superiority of the improved optimized snake model, experiments are carried out in the corresponding database. The experimental results show that the contour of the moving object extracted by the improved snake model algorithm is complete and the segmentation effect is obvious. At the same time, the running speed of the whole algorithm has been significantly improved.

Automated movement recognition to predict motor impairment in high-risk infants: a systematic review of diagnostic test accuracy and meta-analysis.

To assess the sensitivity and specificity of automated movement recognition in predicting motor impairment in high-risk infants. We searched MEDLINE, Embase, PsycINFO, CINAHL, Web of Science, and Scopus databases and identified additional studies from the references of relevant studies. We included studies that evaluated automated movement recognition in high-risk infants to predict motor impairment, including cerebral palsy (CP) and non-CP motor impairments. Two authors independently assessed studies for inclusion, extracted data, and assessed methodological quality using the Quality Assessment of Diagnostic Accuracy Studies-2. Meta-analyses were performed using hierarchical summary receiver operating characteristic models. Of 6536 articles, 13 articles assessing 59 movement variables in 1248 infants under 5months corrected age were included. Of these, 143 infants had CP. The overall sensitivity and specificity for motor impairment were 0.73 (95% confidence interval [CI] 0.68-0.77) and 0.70 (95% CI 0.65-0.75) respectively. Comparatively, clinical General Movements Assessment (GMA) was found to have sensitivity and specificity of 98% (95% CI 74-100) and 91% (95% CI 83-93) respectively. Sensor-based technologies had higher specificity (0.88, 95% CI 0.80-0.93). Automated movement recognition technology remains inferior to clinical GMA. The strength of this study is its meta-analysis to summarize performance, although generalizability of these results is limited by study heterogeneity.

Sports Dance Action Recognition System Oriented to Human Motion Monitoring and Sensing

Because of its high research value, action recognition has become a very popular research direction in recent years. However, the research on the combination of motion recognition technology and dance movements is still in its infancy. At the same time, due to the high complexity of dance movements and the problems of human body self‐occlusion when performing dances, research on dance video action recognition has been caused. Progress is relatively slow. This article mainly introduces the research of sports dance action recognition system oriented to human motion monitoring and sensing, fully considers the abovementioned problems, and makes in‐depth research and analysis on the current excellent action recognition research content in this field. This paper proposes a research method of sports dance movement recognition for human movement monitoring and sensing, including sports dance movement classification algorithm and sports dance movement preprocessing algorithm, which is used to conduct research experiments on sports dance movement recognition for human movement monitoring and sensing. The experimental results of this article show that the average recognition accuracy of the sports dance action recognition system for human motion monitoring and sensing is 92%, which can be used in daily sports dance training and competition.

Human Action Recognition in Smart Cultural Tourism Based on Fusion Techniques of Virtual Reality and SOM Neural Network.

Smart cultural tourism is the development trend of the future tourism industry. Virtual reality is an important tool to realize smart tourism. The reality of virtual reality mainly comes from human-computer interaction, which is closely related to human action recognition technology. Therefore, the research takes human action recognition as the research direction, uses a self-organizing mapping network (SOM) neural network to extract the key frame of action video, combines it with multi-feature vector method to recognize human action, and compares the recognition rate and user satisfaction of different recognition methods. The results show that the recognition rate of multi-feature voting human action recognition algorithm based on SOM neural network is 93.68% on UT-Kinect action, 59.06% on MSRDailyActivity3D, and the overall action recognition time is only 3.59 s. Within six months, the total profit of human-computer interactive virtual reality tourism project with SOM neural network multi-eigenvector as the core algorithm reached 422,000 yuan, and 88% of users expressed satisfaction after use. It shows that the proposed method has a good recognition rate and can give users effective feedback in time. It is hoped that this research has a certain reference value in promoting the development of human motion recognition technology.

Application of human motion recognition technology in extreme learning machine

Human motion recognition is a branch of computer vision research and is widely used in fields like interactive entertainment. Most research work focuses on human motion recognition methods based on traditional video streams. Traditional RGB video contains rich colors, edges, and other information, but due to complex background, variable illumination, occlusion, viewing angle changes, and other factors, the accuracy of motion recognition algorithms is not high. For the problems, this article puts forward human motion recognition based on extreme learning machine (ELM). ELM uses the randomly calculated implicit network layer parameters for network training, which greatly reduces the time spent on network training and reduces computational complexity. In this article, the interframe difference method is used to detect the motion region, and then, the HOG3D feature descriptor is used for feature extraction. Finally, ELM is used for classification and recognition. The results imply that the method proposed here has achieved good results in human motion recognition.

Dance Movement Recognition Technology Based on Multifeature Information Fusion

Multifeature data integration technology uses computer technology to automatically analyze and process the observation data obtained by multiple sensors in chronological order according to a defined threshold. And it can complete the necessary decision‐making and evaluation tasks in the data processing process. This article is aimed at studying the effect of multifeature information fusion on dance movement recognition technology. The article mainly adopts the principle of structural risk mitigation and the image thresholding method to effectively obtain the binarized image of the dancer’s moving image, so as to select an appropriate threshold, use the threshold as its distinguishing standard, and then segment the pixel threshold. The method of multifeature information fusion and dance movement recognition technology is introduced. The performance of image classification algorithms is compared, and the experiment of dance video movement, human behavior recognition, and gait phase recognition are carried out. The experimental data show that in the power identification algorithm of this article, when the peak value is 1000 bits, the experimental data is 256 bits, the analysis and comparison data is 457 bits, and the experimental environment is 812 bits. When the peak value is 2000 bits, the experimental data is 451 bits, the analysis and comparison data is 635 bits, and the experimental environment is 947 bits. The experimental data is 562 bits, the analysis and comparison data is 546 bits, and the experimental environment is 774 bits. The experimental results show that the dance action recognition technique based on multifeature information fusion is studied well.

RETRACTED: Auxiliary research on difficult aerobics exercise training based on fpga and movement recognition technology

Abstract Based on case instruction, traditional training systems are based on past rivalries’ case studies and preparing to give strength preparing to Aerobics, uncommon games. The preparation results can't be assessed shrewdly and precisely, and the exhibition of investigation and dynamic examination is poor. To take care of this issue, the reason for unique high impact exercise estimates dependent on human-made consciousness is to understand a preparation system that focuses on Aerobics, the strength and the quality of particular sports. It can be achieved by researching Convolutional Neural Networks (CNN) model systems, such as optimizations and other intelligent features such as an intelligent Convolutional Neural Network (CNN) decision making. The design system architecture framework includes sensors, receivers, databases, and analysis modules. The system's core chip is the human-computer interaction FPGA control module to provide comprehensive training in impressive aerobics intensity quality. The information-gathering module is used for gathering information about gauges, sports and strength training as a language. Trainee information management and training result statistics and query execution through the information management module. Some system software includes a software configuration diagram of the system and booting and landing the system. Through the work cycle of the investigation module, the power of specific vigorous exercise sports is dissected. Test results show that the planned framework can give ongoing, standard strength preparing for Aerobics, explicit activities and improve preparing productivity.

Network algorithm real-time depth image 3D human recognition for augmented reality

This paper studies the application of augmented reality real-time depth image technology to 3D human motion recognition technology. The accuracy and real-time performance of sensor-based 3D human reconstruction are affected by visual characteristics and illumination changes. Features are not easily extracted and cannot be tracked, leading to failures in the 3D reconstruction of the human body. Based on this system, the sensor-based visual inertial initialization algorithm is studied, which is integrated in the two-frame image time interval to provide accurate initial values for vision-based motion estimation, improve the accuracy of the calculated posture, and finally improve the accuracy of the 3D reconstruction system. Based on the relationship between the depth image and the distance and reflectivity, a model for correcting the distance error and reflectivity error of the depth image is established to improve the accuracy of the depth image, and finally the accuracy of the three-dimensional reconstruction of the human body.

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

The image acquisition system is the main part of acquiring image information, and its performance largely determines the accuracy and difficulty of subsequent planning. The main purpose of this article is to design an image acquisition system for aerobics training based on motion recognition technology. This article mainly introduces the teaching experiments based on the design of aerobics courses. Through the empirical investigation and analysis of the impact of relevant experimental data on aviation training courses, attempts to establish the connection between aerobics social adaptability and various dimensions The cultivation of student’s social adaptability. In this paper, the GMM algorithm is mainly used to distinguish the rest time when the action occurs, and the subsequent rest period is used as the basis for segmenting multiple events in the action sequence. Finally, the characteristics of the action coding mapping of each event are derived, and the support vector machine is used to complete the energy recognition process of the existence of a single energy. The experimental results of this paper show that the designed embedded image acquisition system has high integration and stability, the acquired image resolution is 640x480, and the wireless transmission rate is 5MbPs, which has wide application prospects.