The utility of machine learning in understanding the motor system is promising a revolution in how to collect, measure, and analyze data. The field of movement science already elegantly incorporates theory and engineering principles to guide experimental work, and in this review we discuss the growing use of machine learning: from pose estimation, kinematic analyses, dimensionality reduction, and closed-loop feedback, to its use in understanding neural correlates and untangling sensorimotor systems. We also give our perspective on new avenues, where markerless motion capture combined with biomechanical modeling and neural networks could be a new platform for hypothesis-driven research.

机器学习在理解电机系统方面的效用有望在如何收集、测量和分析数据方面带来一场革命。运动科学领域已经优雅地结合了理论和工程原理来指导实验工作，在这篇评论中，我们讨论了机器学习越来越多的应用：从姿势估计、运动学分析、降维和闭环反馈，到它在了解神经相关性和解开感觉运动系统。我们还给出了我们对新途径的看法，其中无标记运动捕捉与生物力学建模和神经网络相结合可能成为假设驱动研究的新平台。