Application of gaming robot based on gait recognition algorithm in sports training and assistance system

• Published in: Entertainment computing Vol. 52; p. 100763
• Main Authors: Hu, Liangnan, Liu, Changjiang, Jiang, Wei, He, Dai, Qu, Qiang, Wang, Zhen, Ying, Chenlin
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.01.2025
• Subjects: Feature aggregation; Gait recognition; Game sports robot; Sports assistance; Game sports robot; Sports assistance; Feature aggregation; Gait recognition
• ISSN: 1875-9521; 1875-953X
• DOI: 10.1016/j.entcom.2024.100763

•This article analyzes the application of game motion robots based on gait recognition algorithms in sports training and motion assistance systems.•We combine gait recognition algorithms with video feature aggregation to integrate high-level and low-level network features, achieving effective extraction of multi-level image features.•The motion assistance system designed in this article can automatically determine the movements during exercise. At present, game sports robots have become a new type of sports assistance technology, which can achieve more natural and smooth motion trajectory analysis under continuous training, and provide a gamified user experience for sports training. This article analyzes the application of game motion robots based on gait recognition algorithms in sports training and motion assistance systems, which can more accurately obtain information data of the athlete during the movement process and has good results in preventing sports injuries. We combine gait recognition algorithms with video feature aggregation to integrate high-level and low-level network features, achieving effective extraction of multi-level image features. Finally, complete action recognition, review, and analysis. Through experimental comparison, it can be seen that the feature aggregation modules designed in this article at different levels are very effective, and combined with gait recognition algorithms, they can effectively improve the accuracy of motion recognition and have excellent robustness. The motion assistance system designed in this article can automatically determine the movements during exercise, allowing athletes to self-evaluate their movements and correct incorrect movements without the need for professional guidance, achieving healthy and effective exercise results.