Afzal H M Rehan, Louhichi Borhen, Alrasheedi Nashmi H
Key Laboratory for Space Bioscience and Biotechnology, Engineering Research Center of Chinese Ministry of Education for Biological Diagnosis, Treatment and Protection Technology and Equipment, School of Life Sciences, Northwestern Polytechnical University, Xi'an 710072, China.
Deanship of Scientific Research, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia.
Bioengineering (Basel). 2025 Apr 27;12(5):461. doi: 10.3390/bioengineering12050461.
The evolution of motion capture technology from marker-based to marker-less systems is a promising field, emphasizing the critical role of combining electromyography (EMG), joint moments, and ground reaction forces (GRF) in advancing biomechanical analysis. This review examines the integration of EMG, joint moments, and GRF in marker-less video-based motion capture systems, focusing on current approaches, challenges, and future research directions. This paper recognizes the significant challenges of integrating the aforementioned modalities, which include problems of acquiring and synchronizing data and the issue of validating results. Particular challenges in accuracy, reliability, calibration, and environmental influences are also pointed out, together with the issue of the standard protocols of multimodal data fusion. Using a comparative analysis of significant case studies, the review examines existing methodologies' successes and weaknesses and established best practices. New emerging themes of machine learning techniques, real-time analysis, and advancements in sensing technologies are also addressed to improve data fusion. By highlighting both the limitations and potential advancements, this review provides essential insights and recommendations for future research to optimize marker-less motion capture systems for comprehensive biomechanical assessments.
运动捕捉技术从基于标记的系统向无标记系统的演进是一个很有前景的领域，它强调了将肌电图（EMG）、关节力矩和地面反作用力（GRF）相结合在推进生物力学分析方面的关键作用。本综述探讨了EMG、关节力矩和GRF在基于无标记视频的运动捕捉系统中的整合，重点关注当前的方法、挑战和未来的研究方向。本文认识到整合上述模态所面临的重大挑战，包括数据采集与同步问题以及结果验证问题。还指出了在准确性、可靠性、校准和环境影响方面的特殊挑战，以及多模态数据融合的标准协议问题。通过对重要案例研究的比较分析，本综述考察了现有方法的成功与不足，并确立了最佳实践。还讨论了机器学习技术、实时分析和传感技术进步等新出现的主题，以改进数据融合。通过突出局限性和潜在进展，本综述为未来研究提供了重要见解和建议，以优化用于全面生物力学评估的无标记运动捕捉系统。
