scholarly journals Digital Twins by Physical Education Teaching Practice in Visual Sensing Training System

2022 ◽  
Vol 2022 ◽  
pp. 1-12
Author(s):  
Xinran Liu ◽  
Ji Jiang
Keyword(s):  
Physical Education ◽  
Teaching Practice ◽  
Gait Recognition ◽  
Recognition Rate ◽  
Recognition Algorithm ◽  
Training System ◽  
Digital Twins ◽  
Analysis Experiment ◽  
Cloud Server ◽  
Joint Monitoring

The paper expects to improve the efficiency and intelligence of somatosensory recognition technology in the application of physical education teaching practice. Firstly, the combination of induction recognition technology and the Internet is used. Secondly, through the Kinect sensor, bone data are acquired. Finally, the hidden Markov model (HMM) is used to simulate the experimental data. On the simulation results, a gait recognition algorithm is proposed. The gait recognition algorithm is used to identify the motion behaviour, and the results are displayed in the Web (World Wide Web) end built by the cloud server. Meantime, in view of the existing problems in the practice of physical education, combined with the establishment and operation of the Digital Twins (DTs) system, the camera source recognition architecture is carried out since the twin network and the two network branches share weights. This paper analyses these problems since the application of somatosensory recognition technology and puts forward the improvement methods. For the single problem of equipment in physical education, this paper puts forward the monitoring and identification function of the cloud server. It is to transmit data through Hypertext Transfer Protocol (HTTP) and locate and collect data through a monitoring terminal. For the lack of comprehensiveness and balance of sports plans, this paper proposes a scientific training plan and process customization based on Body Mass Index (BMI), analyses real-time data in the cloud, and makes scientific customization plans according to different students’ physical conditions. Moreover, 25 participants are invited to carry out the exercise detection and analysis experiment, and the joint monitoring of their daily movements is tested. This process has completed the design of a feasible and accurate platform for information collection and processing, which is convenient for managers and educators to comprehensively and scientifically master and manage the physical level and training of college students. The proposed method improves the recognition rate of the camera source to some extent and has important exploration significance in the field of action recognition.

Design of Gait Recognition System Based on FPGA and DSP

2014 ◽  
Vol 687-691 ◽  
pp. 3861-3868
Author(s):  
Zheng Hong Deng ◽  
Li Tao Jiao ◽  
Li Yan Liu ◽  
Shan Shan Zhao
Keyword(s):  
Monitoring System ◽  
Nearest Neighbor ◽  
Gait Recognition ◽  
Recognition Rate ◽  
Digital Signal ◽  
Recognition System ◽  
Recognition Algorithm ◽  
Zernike Moment ◽  
Intelligent Monitoring ◽  
The One

According to the trend of the intelligent monitoring system, on the basis of the study of gait recognition algorithm, the intelligent monitoring system is designed based on FPGA and DSP; On the one hand, FPGA’s flexibility and fast parallel processing algorithms when designing can be both used to avoid that circuit can not be modified after designed; On the other hand, the advantage of processing the digital signal of DSP is fully taken. In the feature extraction and recognition, Zernike moment is selected, at the same time the system uses the nearest neighbor classification method which is more mature and has good real-time performance. Experiments show that the system has high recognition rate.

Gait Recognition Algorithm Based on the Trajectory of Tiptoe

2011 ◽  
Vol 255-260 ◽  
pp. 1984-1988
Author(s):  
Yi Bo Li ◽  
Qin Yang
Keyword(s):  
Wavelet Packet ◽  
Gait Recognition ◽  
Recognition Rate ◽  
Recognition Algorithm ◽  
Small Sample ◽  
Knee Joints ◽  
Support Vector ◽  
Gait Characteristics ◽  
Camera Angle ◽  
Academy Of Sciences

Most researchers focus on the gait characteristics of hip and changed angle of knee joints, gait characteristics of foot is still less attention, also apply wavelet packet to analysis more detailed information of characteristics’ data, and use the support vector machine algorithm to reduce the randomness, it has their unique advantages in the small sample. Summarized the above three points of the paper, the paper proposes a new gait recognition method to extract trajectory of tiptoe, uses wavelet packet to analyze it, then applies SVM for classification and recognition. Tested at the NLPR database of Chinese Academy of Sciences of 45 camera angle, we observed that the recognition rate has significantly increased, we observed that the algorithm is an effective identification method.

Human action recognition using simple geometric features and a finite state machine

2013 ◽  
Vol 18 (2-3) ◽  
pp. 49-60 ◽  
Author(s):  
Damian Dudzńiski ◽  
Tomasz Kryjak ◽  
Zbigniew Mikrut
Keyword(s):  
Action Recognition ◽  
Finite State Machine ◽  
Recognition Rate ◽  
Human Action Recognition ◽  
Human Action ◽  
Video Stream ◽  
State Machine ◽  
Recognition Algorithm ◽  
Finite State ◽  
Correct Recognition Rate

Abstract In this paper a human action recognition algorithm, which uses background generation with shadow elimination, silhouette description based on simple geometrical features and a finite state machine for recognizing particular actions is described. The performed tests indicate that this approach obtains a 81 % correct recognition rate allowing real-time image processing of a 360 X 288 video stream.

scholarly journals Physical Activity Recommendations in the Context of New Calls for Change in Physical Education

2021 ◽  
Vol 18 (3) ◽  
pp. 1177
Author(s):  
Karel Frömel ◽  
Jana Vašíčková ◽  
Krzysztof Skalik ◽  
Zbyněk Svozil ◽  
Dorota Groffik ◽  
...  
Keyword(s):  
Physical Activity ◽  
Physical Education ◽  
Teaching Practice ◽  
Social Health ◽  
Physical Strain ◽  
School Practice ◽  
School Based ◽  
Adequate Response ◽  
Physical Activity Recommendations ◽  
Activity Recommendations

The current social, health, and educational changes in society require an adequate response in school-based physical activity (PA), including physical education (PE) lessons. The objective of this study was to identify the real average step counts of Czech and Polish adolescents during PE lessons, and propose recommendations for improving PE programs. This research was carried out in 143 Czech and 99 Polish schools. In the research, a total of 4911 adolescents aged 12–18 years were analyzed as part of teaching practice and 1827 in the context of habitual school practice. Steps were monitored using pedometers. The average step count per PE lesson was 2390 in Czech and Polish boys, while girls achieved 1851 steps. In both countries, boys were subject to greater physical strain in PE lessons compared to girls, both in teaching practice (F(4088,3) = 154.49, p < 0.001, ηp2 = 0.102) and school practice (F(1552,3) = 70.66, p < 0.001, ηp2 = 0.103). Therefore, the priority in PE lessons is to increase the amount of PA for girls, achieve the objectives of PE during PA, and use wearables to improve awareness of PA and improve physical literacy, as well as to support hybrid and online PE as a complement to traditional PE.

Research on Modulation Recognition Algorithm of Digital Communication Signal Based on Wavelet Denoising

2014 ◽  
Vol 608-609 ◽  
pp. 459-467 ◽  
Author(s):  
Xiao Yu Gu
Keyword(s):  
Pattern Recognition ◽  
Feature Extraction ◽  
Decision Theory ◽  
Recognition Rate ◽  
Recognition Algorithm ◽  
Modulation Recognition ◽  
The Core ◽  
Feature Parameters ◽  
Communication Signal ◽  
Modulation Signal

The paper researches a recognition algorithm of modulation signal and modulation modes. The modulation modes to be recognized include 2ASK, 2FSK, 2PSK, 4ASK, 4FSK and 4PSK modulation. There are two methods recognizing modulation modes of digital signal, method based on decision theory and pattern-recognition method based on feature extraction. The method based on decision theory is not suitable for recognition with multiple modulation modes. The core of pattern recognition based on feature extraction is selection of feature parameters. So the paper uses the feature parameters with simple calculation, easy to be implemented and high recognition rate as the core. The extraction of feature parameters is based on instant feature of modulation signal after Hilbert transformation.

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