Abstract：
This paper summarises recent advancement in applications of machine learning in sports biomechanics to bridge the lab-to-field gap as presented in the Hans Gros Emerging Researcher Award lecture at the annual conference of the International Society of Biomechanics in Sports 2022. One major challenge in machine learning applications is the need for large, high-quality datasets. Currently, most datasets, which contain kinematic and kinetic information, were collected using traditional laboratory-based motion capture despite wearable inertial sensors or standard video cameras being the hardware capable of on-field analysis. For both technologies, no high-quality large-scale databases exist. A second challenge is the lack of guidelines on how to use machine learning in biomechanics, where mostly small datasets collected on a particular population are available. This paper will summarise methods to re-purpose motion capture data for machine learning applications towards on-field motion analysis and give an overview of current applications in an attempt to derive guidelines on the most appropriate algorithm to use, an appropriate dataset size, suitable input data to estimate motion kinematics or kinetics, and how much variability should be in the dataset. This information will allow research to progress towards bridging the lab-to-field gap.
摘要：
本文总结了机器学习在运动生物力学中的应用的最新进展，以弥合实验室到现场的差距，如在2022年国际运动生物力学学会年会上的汉斯·格罗斯新兴研究员奖演讲中所述。机器学习应用的一个主要挑战是需要大量的,高质量的数据集。目前,大多数数据集，包含运动学和动力学信息，尽管可穿戴惯性传感器或标准摄像机是能够进行现场分析的硬件，但还是使用传统的基于实验室的运动捕获来收集。对于这两种技术，没有高质量的大规模数据库。第二个挑战是缺乏关于如何在生物力学中使用机器学习的指导方针，在那里，在特定人群上收集的大多数小数据集都是可用的。本文将总结将运动捕获数据重新用于机器学习应用的方法，以进行现场运动分析，并概述当前的应用，以尝试得出最合适的算法指南。适当的数据集大小，合适的输入数据来估计运动运动学或动力学，以及数据集中应该有多少可变性。这些信息将使研究朝着弥合实验室与现场差距的方向发展。
