Image detection and basketball training performance simulation based on improved machine learning

Wang, Pengyu; Wanna, Gao

doi:10.3233/jifs-189243

Cited by 11 publications

(5 citation statements)

References 18 publications

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“…Based on ( 1 ), this paper gives the deterministic strategy formula of multiple Q network structures. Because the multiple Q network structure adopts a random strategy, to obtain the current action, it is necessary to sample the probability distribution of the optimal strategy [ 6 ]. The entire action space is integrated at each step in the iterative process.…”

Section: Model Architecture Designmentioning

confidence: 99%

Sports Rehabilitation Treatment of Medical Information in Tertiary Hospitals Based on Computer Machine Learning

Zhang

2022

Computational Intelligence and Neuroscience

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Objective. The processing and analysis of medical rehabilitation information data in tertiary hospitals is a hot research topic. Combining medical data analysis with machine learning algorithms to improve data mining efficiency is a problem that needs to be solved at present. This paper proposes an autonomous perception model of sports medicine rehabilitation equipment based on a deep learning algorithm for sports medical rehabilitation data. Methods. This paper cites a deep learning multi-dimensional perception model for medical rehabilitation equipment autonomous perception. The model utilizes the automatic overhaul of medical rehabilitation equipment based on deep belief networks. This paper extracts features through a multi-layer neural network and obtains fault location results of medical rehabilitation equipment through softmax. Results. In similarity prediction, the accuracy rate of the first three kinds of feedback containing the target answer is 77%. The accuracy rate of the target answers included in the top five kinds of feedback was 92%. Conclusion. In this study, it is feasible to apply deep learning to the quality control information system of sports rehabilitation medical equipment. This improves the management efficiency of medical rehabilitation equipment to a certain extent.

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Section: Model Architecture Designmentioning

confidence: 99%

Sports Rehabilitation Treatment of Medical Information in Tertiary Hospitals Based on Computer Machine Learning

Zhang

2022

Computational Intelligence and Neuroscience

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“…There are however some disadvantages to using this approach. As a result, it is critical for the development of athletes' competitive abilities and coaches' decision-making abilities that sports data and motions of players can be precisely captured in real time [9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Application of Artificial Intelligence and Big Data Technology in Basketball Sports Training

Zhong

2022

Wireless Communications and Mobile Computing

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Basketball is a sport with a wide range of complicated human movements, and the ability to accurately identify these movements is critical in both competition and training. Training athletes is largely based on the subjective observations and experiences of the coaches. Artificial intelligence and big data technologies can be used to monitor athlete training. It can also assist coaches in making decisions which significantly increase the athletic ability by detecting their movements. This research work proposed a mechanism called BSTARNet which is an action recognition method for basketball sports training. The method is based on artificial neural network (ANN), and network is trained through basketball sports big data. First, this work uses the Convolution Long Short-Term Memory (ConvLSTM) unit to extract the spatiotemporal information features of basketball sports training from videos. Second, this work establishes the Attention Long Short-Term Memory (AttLSTM) unit that combines the attention mechanism with the LSTM. The unit selectively scans each location, giving more attention to the area where the action takes place. Finally, the network framework is built by improving the ordinary encoder-decoder model. After that, the spatiotemporal information contained in the video is encoded based on the Darknet network model. In the decoding stage, the AttLSTM structure is used to replace the ordinary LSTM. These units are combined to form the BSTARNet architecture. Experiments are conducted to verify the effectiveness of the proposed method applied on action recognition in basketball sports training and achieved 89.5% mAP and 95.4% accuracy.

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“…By comparing the simulation results with real training videos of athletes, he displayed training movements and standard movements on the same screen and conducted a comparative analysis of the movements [3]. Pengyu and Wanna research mainly took the characteristic information of basketball in the state of basketball goal as the starting point, and compared and analyzed the detection methods by detecting the target in the environment [4]. Ma proposed and improved the optimization method of basketball skills and teaching mode based on visual action simulation.…”

Section: Introductionmentioning

confidence: 99%

Simulation on Basketball Recognition Based on Mobile Network Communication and Panoramic Vision

Yang

2022

International Transactions on Electrical Energy Systems

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At present, the application of information technology has realized the transformation of people’s production and lifestyle, and it has also promoted the transformation of the sports industry. At present, the application of informatization in the basketball field in the sports industry is increasing. By using the advanced methods and technologies of its information display, this article aims at realizing the optimization of the basketball recognition pattern in the mobile network communication environment to promote the ecological development of the sports industry. It proposes to use panoramic vision to guide the integration of computer network and basketball field, which is helpful to analyze and solve the objectivity problems of single action and system incompatibility in basketball recognition simulation. By referring to the principles and laws of panoramic vision, the identification module and simulation module of the basketball auxiliary training system are constructed and optimized, so as to realize the promotion of basketball and the ecologicalization of teaching. In the research on the basketball recognition simulation system, the three-dimensional skeleton key point coordinate data are input into the ST-GCN network for comparison and testing. Specifically, it includes 2D key point coordinate data and 3D key point coordinate data as input to conduct model research and testing in the self-made basketball dataset. The experimental results show that the recognition rates of 2D and 3D coordinates are 66.64% and 87.69%, respectively. Therefore, it is crucial to use the human panoramic vision algorithm to convert 2D coordinates to 3D coordinates.

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Image detection and basketball training performance simulation based on improved machine learning

Cited by 11 publications

References 18 publications

Sports Rehabilitation Treatment of Medical Information in Tertiary Hospitals Based on Computer Machine Learning

Sports Rehabilitation Treatment of Medical Information in Tertiary Hospitals Based on Computer Machine Learning

Application of Artificial Intelligence and Big Data Technology in Basketball Sports Training

Simulation on Basketball Recognition Based on Mobile Network Communication and Panoramic Vision

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