机器学习助力步态与运动生物力学分析，提升动作识别与数据洞察力。
Machine Learning in Biomechanics: Key Applications and Limitations in Walking, Running, and Sports Movements
- 利用姿态估计、事件检测等方法自动分析步态与运动
- 面临数据稀缺与标注不足的挑战，影响模型泛化能力
- 适合生物力学、体育科学及康复工程领域的研究者参考
本章综述了机器学习在步态（行走与跑步）和运动生物力学中的最新应用，包括姿态估计、特征估计、事件检测、数据探索与聚类、自动化分类等。文章探讨了机器学习在解决生物力学工作流挑战方面的潜力，强调了数据与标注获取困难、模型可解释性不足等核心限制，并指出跨学科合作对充分挖掘其应用潜能的重要性。
原文摘要 · Abstract (English)
This chapter provides an overview of recent and promising Machine Learning applications, i.e. pose estimation, feature estimation, event detection, data exploration & clustering, and automated classification, in gait (walking and running) and sports biomechanics. It explores the potential of Machine Learning methods to address challenges in biomechanical workflows, highlights central limitations, i.e. data and annotation availability and explainability, that need to be addressed, and emphasises the importance of interdisciplinary approaches for fully harnessing the potential of Machine Learning in gait and sports biomechanics.
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