Article: Swimming-assisted training and physical fitness enhancement system based on improved YOLOv5 and improved ST-GCN Journal: International Journal of Information and Communication Technology (IJICT) 2026 Vol.27 No.37 pp.29 - 50 Abstract: This paper proposes a swimmer action recognition model combining an improved YOLOv5 algorithm and an improved ST-GCN, and develops a swimming-assisted training system based on this model. The system comprises four key modules: posture recognition, data visualisation, training feedback, and physical fitness assessment. Experimental results demonstrate that the proposed model achieves 98.78% precision, 98.13% average accuracy, and 97.58% recall, outperforming comparison models. The system exhibits superior performance with a mean recognition time of 79.6 ms and CPU occupation of 42.57%. Practical application evaluation shows significant advantages in improving training effectiveness, with 237 requests per second throughput, 12.41% memory usage, and coach/athlete satisfaction rates of 98.76% and 98.47%, respectively. Inderscience Publishers - linking academia, business and industry through research

Title: Swimming-assisted training and physical fitness enhancement system based on improved YOLOv5 and improved ST-GCN

Authors: Yang Chen

Addresses: Spanish Sports University, Universidad Católica San Antonio de Murcia, Guadalupe, 30107, Murcia, Spain

Abstract: This paper proposes a swimmer action recognition model combining an improved YOLOv5 algorithm and an improved ST-GCN, and develops a swimming-assisted training system based on this model. The system comprises four key modules: posture recognition, data visualisation, training feedback, and physical fitness assessment. Experimental results demonstrate that the proposed model achieves 98.78% precision, 98.13% average accuracy, and 97.58% recall, outperforming comparison models. The system exhibits superior performance with a mean recognition time of 79.6 ms and CPU occupation of 42.57%. Practical application evaluation shows significant advantages in improving training effectiveness, with 237 requests per second throughput, 12.41% memory usage, and coach/athlete satisfaction rates of 98.76% and 98.47%, respectively.

Keywords: YOLOv5; spatial-temporal graph convolutional network; ST-GCN; swimming; assisted training; physical fitness enhancement.

DOI: 10.1504/IJICT.2026.153265

International Journal of Information and Communication Technology, 2026 Vol.27 No.37, pp.29 - 50

Received: 17 Oct 2025
Accepted: 08 Jan 2026
Published online: 29 Apr 2026 *

Title: Swimming-assisted training and physical fitness enhancement system based on improved YOLOv5 and improved ST-GCN

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