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Digital human and embodied intelligence for sports science: advancements, opportunities and prospects

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Abstract

This paper presents a comprehensive review of state-of-the-art motion capture techniques for digital human modeling in sports, including traditional optical motion capture systems, wearable sensor capture systems, computer vision capture systems, and fusion motion capture systems. The review explores the strengths, limitations, and applications of each technique in the context of sports science, such as performance analysis, technique optimization, injury prevention, and interactive training. The paper highlights the significance of accurate and comprehensive motion data acquisition for creating high-fidelity digital human models that can replicate an athlete’s movements and biomechanics. However, several challenges and limitations are identified, such as limited capture volume, marker occlusion, accuracy limitations, lack of diverse datasets, and computational complexity. To address these challenges, the paper emphasizes the need for collaborative efforts from researchers and practitioners across various disciplines. By bridging theory and practice and identifying application-specific challenges and solutions, this review aims to facilitate cross-disciplinary collaboration and guide future research and development efforts in harnessing the power of digital human technology for sports science advancement, ultimately unlocking new possibilities for athlete performance optimization and health.

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Funding

National Natural Science Foundation of China (grant number: 62077037) and Research and Innovation Grant for Graduate Students, Shanghai University of Sport (Project Number: YJSCX-2023-034).

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  1. Shanghai University of Sport, Shanghai, 200438, China

    **ang Suo, Weidi Tang, Lijuan Mao & Zhen Li

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  1. **ang Suo
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X.S. and W.T. wrote the main manuscript text and prepared the figures and tables. Z.L. and L.M. contributed to supervision and project administration. All authors reviewed the manuscript.

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Correspondence to Zhen Li.

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Suo, X., Tang, W., Mao, L. et al. Digital human and embodied intelligence for sports science: advancements, opportunities and prospects. Vis Comput (2024). https://doi.org/10.1007/s00371-024-03547-4

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