Title: Enhancing physical education with kinect motion tracking and context personalisation

Authors: Yuqiu Zhang

Addresses: Jilin Agricultural Science and Technology University, 132109, Jilin, China

Abstract: This study proposes a novel approach for physical education (PE) that integrates kinect motion tracking, deep learning, and context personalisation. The system combines real-time feedback and adaptive learning paths to optimise student participation, motivation, and physical skill development. An ablation study was conducted to compare the effectiveness of the full system with three other configurations: kinect-only motion tracking, kinect with context personalisation, and kinect with deep learning. The experimental results indicate that the full system, which combines all three components, significantly outperforms the other configurations in terms of motivation, physical performance improvement, and engagement. Specifically, the full system achieved the highest improvement in skill development (90%), engagement (98%), and motivation, suggesting that the combination of kinect motion tracking, context personalisation, and deep learning is most effective for enhancing PE outcomes. This research contributes to the digital transformation of physical education. It provides a new pathway to leverage technology for improving both student motivation and performance.

Keywords: physical education; kinect; deep learning; context personalisation; teaching methods.

DOI: 10.1504/IJBIDM.2026.152476

International Journal of Business Intelligence and Data Mining, 2026 Vol.28 No.2/3, pp.152 - 167

Received: 17 May 2025
Accepted: 09 Sep 2025
Published online: 23 Mar 2026 *

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