Sports Training Strategies and Interactive Control Methods Based on Neural Network Models

Li, Xulei; Li, Yupeng

doi:10.1155/2022/7624578

Cited by 8 publications

(4 citation statements)

References 19 publications

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“…The performance of the proposed correlation method is analyzed using experimental analysis using the data source in [32]. This data source analyzes the WS data from 9 sensors placed at different parts of a human body for its correlation.…”

Section: Resultsmentioning

confidence: 99%

Retracted: A new machine learning approach based on fuzzy data correlation for recognizing sports activities

et al. 2024

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Wearable sensors (WS) played a vital role in health assistance to improve the patient monitoring process. However, the existing data collection process faces difficulties in error corrections, rehabilitation, and training validations. Therefore, the fuzzy data analysis requires additional effort to reduce the overall problems in sports rehabilitation. The research difficulties are overcome by applying the spatial correlation with a support vector machine (SDC-SVM). The algorithm uses the hyperplane function that recognizes the sports person activities and improves overall activity recognition efficiency. The sensor data is analyzed according to the input margin, and the classification process is performed. In addition, feature correlation and input size are considered to maximize the overall classification procedure of WS data correlation using the size and margin of the input and previously stored data. In both the differentiation and classification instances, the data's spatiotemporal features are extracted and analyzed using support vectors. This proposed method can improve the accuracy of recognition, F1 score, and computing time for the varying WS inputs, classifications, and subjects.

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Section: Resultsmentioning

confidence: 99%

Retracted: A new machine learning approach based on fuzzy data correlation for recognizing sports activities

et al. 2024

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“…During the actual operation of the system, various training parameters fluctuate randomly within a certain range; for example, the quality coefficient of pedaling power fluctuates around 0.9. e fluctuations of these training parameter indicators interact with each other, which is a fuzzy relationship with the training load level as the goal. e athlete adjusts the changes of relevant training parameters in a timely manner according to the feedback navigation instruction information to stabilize it within the area corresponding to the target load level [20]. When the fluctuations of various training parameters under a certain training load level gradually decrease and become stable, it means that the training level of the athlete's current load level has improved.…”

Section: End Submentioning

confidence: 99%

Application of Wireless Network Multisensor Fusion Technology in Sports Training

Tang

2022

Computational Intelligence and Neuroscience

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Fencing is an advantageous event for a country to participate in the Asian Games. The discussion and research on the means and methods of special fencing ability training are of great significance to the further development of fencing in the country. Based on the multisensor information fusion technology, this paper develops a special fencing training system with digital monitoring and intelligent decision-making. The multisensor information fusion technology scheme, which is reported in this paper, is used to perform decision-level fusion based on fuzzy inference technology whereas Kalman filter is used to optimize the original information. On the basis of the overall structure, mechanical mechanism, and performance analysis, the mechanical prototype of the fencing special ability training system was developed whereas design, processing, and debugging of the prototype were completed. Combined with the mechanical prototype design of various index parameter display structures based on multisensor information fusion technology, the digital processing of the fencing special ability training system is realized. The force, speed, position, and other characteristic quantities in the fencing special training system are collected by the pressure and rotational speed sensors respectively whereas USB communication technology is used for data transmission. On the basis of in-depth analysis of the characteristics of the special training system for fencing, an abstract system of pedaling power based on the center of gravity and trend models is designed where the Kalman filter is reserved for the special application fields. Moreover, the core role of Kalman filter is carried out when original data information is needed to be obtained. By constructing a force distribution pressure center trajectory measurement model, a training load and training parameter measurement model, and a training level evaluation model, the data-level raw information is specially processed to generate more training parameters that meet specific characteristics, making the multisensor information fusion technology more efficient. It is well integrated into the fencing special ability training system. By applying fuzzy reasoning technology, the special training parameters and the relationship between these parameters are transformed into fuzzy sets and fuzzy rules, and the perceptual knowledge of sports training experience is transformed into fuzzy rules. With this decision-level data fusion method, the fencing special training system has certain intelligent functions.

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“…It implements the self-Modification diminishing algorithm, which was enhanced by the convolution's self-coding mechanism (SCM).In order to develop a modern health information network for sports medicine, the CNN makes the processing of complicated athletic health data simpler and completes a cloud-based circuit model. Study [17] suggested an interactive control approach for sports training that raises the standard for instruction using the deep fusion of data and increases human-machine interaction.The aggregation of wireless sensors and neural networks for deep learning techniques led to a current revolution in sports training methods. Theyallow for the development of more efficient and scientific sports methods.…”

Section: Related Workmentioning

confidence: 99%

Deep learning in sports skill learning: a case study and performance evaluation

Lian

2024

EAI Endorsed Trans Perv Health Tech

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Deep learning in sports uses neural networks to evaluate data from sensors and cameras, providing coaches and players insights to enhance training methods and performance. Sports skill development include issues with data availability, trouble interpreting methods for coaching purposes, possible financial constraints for players and regional sports teams. To overcome this, we proposed an Artificial Hummingbird Optimized XGBoost (AHO-XGB) to provide accurate predictions and analysis of an athlete's performance.In this study, the research consists of 20 faculty members and 250 learners from 3 universities.Many sports talents are currently taught to students in famous colleges and universities, but they truly become proficient in the skills. To evaluate the performance of the proposed method in terms of accuracy (92.6%), precision (90.5%), and recall (94.3%). The outcome of this research in sports skill learning transforms performance and training analysis by examining large amounts of data and offering suggestions for skill development.

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Sports Training Strategies and Interactive Control Methods Based on Neural Network Models

Cited by 8 publications

References 19 publications

Retracted: A new machine learning approach based on fuzzy data correlation for recognizing sports activities

Retracted: A new machine learning approach based on fuzzy data correlation for recognizing sports activities

Application of Wireless Network Multisensor Fusion Technology in Sports Training

Deep learning in sports skill learning: a case study and performance evaluation

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