Adaptive Recognition of Motion Posture in Sports Video Based on Evolution Equation

Yuan, Rui; Zhang, Zhendong; Le, Yanyan; Chen, Enqing

doi:10.1155/2021/2148062

Cited by 9 publications

(1 citation statement)

References 39 publications

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“…e synthetic result with the fuzzy matrix R is (0.11, 0.5, 0.93, 0.53, 0.5, 0, 0), and the element with the largest membership degree in the fuzzy set is selected as the nal result control amount, which is NS [19].…”

Section: Load Levelmentioning

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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Section: Load Levelmentioning

confidence: 99%