Enhancing physical education with kinect motion tracking and context personalisation

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.

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Publication Details

Journal
International Journal of Business Intelligence and Data Mining
Published
2026-01-01
DOI
https://doi.org/10.1504/ijbidm.2026.152476
Primary Topic
Physical Education and Pedagogy
Type
article
Field-Weighted Citation Impact
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article

Enhancing physical education with kinect motion tracking and context personalisation

Yuqiu Zhang
International Journal of Business Intelligence and Data Mining
Physical Education and Pedagogy
article

Enhancing physical education with kinect motion tracking and context personalisation

Yuqiu Zhang
article en

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.

International Journal of Business Intelligence and Data MiningVol. 28(2/3)
Jilin Agricultural Science and Technology University (CN)
Quality Education
Openalex Percentile: Top 10%
Physical Education and Pedagogy
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Enhancing physical education with kinect motion tracking and context personalisation — Yuqiu Zhang · International Journal of Business Intelligence and Data Mining (2026) | TGRS Research Map | TGRS