Sports Medicine, Sports Surgery Clinic, Dublin, Ireland.
Department of Life Sciences, University of Roehampton, London, United Kingdom.
PLoS One. 2019 Jul 23;14(7):e0206024. doi: 10.1371/journal.pone.0206024. eCollection 2019.
Motion analysis systems are widely employed to identify movement deficiencies-e.g. patterns that potentially increase the risk of injury or inhibit performance. However, findings across studies are often conflicting in respect to what a movement deficiency is or the magnitude of association to a specific injury. This study tests the information content within movement data using a data driven framework that was taught to classify movement data into the classes: NORM, ACLOP and ACLNO OP, without the input of expert knowledge. The NORM class was presented by 62 subjects (124 NORM limbs), while 156 subjects with ACL reconstruction represented the ACLOP and ACLNO OP class (156 limbs each class). Movement data from jumping, hopping and change of direction exercises were examined, using a variety of machine learning techniques. A stratified shuffle split cross-validation was used to obtain a measure of expected accuracy for each step within the analysis. Classification accuracies (from best performing classifiers) ranged from 52 to 81%, using up to 5 features. The exercise with the highest classification accuracy was the double leg drop jump (DLDJ; 81%), the highest classification accuracy when considering only the NORM class was observed in the single leg hop (81%), while the DLDJ demonstrated the highest classification accuracy when considering only for the ACLOP and ACLNO OP class (84%). These classification accuracies demonstrate that biomechanical data contains valuable information and that it is possible to differentiate normal from rehabilitating movement patterns. Further, findings highlight that a few features contain most of the information, that it is important to seek to understand what a classification model has learned, that symmetry measures are important, that exercises capture different qualities and that not all subjects within a normative cohort utilise 'true' normative movement patterns (only 27 to 71%).
运动分析系统被广泛用于识别运动缺陷，例如可能增加受伤风险或抑制表现的模式。然而，由于对运动缺陷的定义或与特定损伤的关联程度存在差异，不同研究的结果往往相互矛盾。本研究使用数据驱动框架测试运动数据中的信息含量，该框架在没有专家知识输入的情况下，被教导将运动数据分类为 NORM、ACLOP 和 ACLNO OP 类别。NORM 类别由 62 名受试者（124 个 NORM 肢体）呈现，而 156 名接受 ACL 重建的受试者代表 ACLOP 和 ACLNO OP 类别（每个类别 156 个肢体）。研究使用各种机器学习技术检查了跳跃、跳跃和变向运动的数据。分层洗牌交叉验证用于为分析中每个步骤获得预期准确性的度量。使用多达 5 个特征的分类精度（来自表现最佳的分类器）范围为 52%至 81%。分类精度最高的运动是双腿跳下跳（DLDJ；81%），仅考虑 NORM 类时观察到的单腿跳跃的分类精度最高（81%），而仅考虑 ACLOP 和 ACLNO OP 类时 DLDJ 的分类精度最高（84%）。这些分类精度表明，生物力学数据包含有价值的信息，并且可以区分正常和康复运动模式。此外，研究结果强调，少数特征包含大部分信息，了解分类模型学到了什么很重要，对称性度量很重要，运动捕捉不同的质量，并非所有正常队列中的受试者都使用“真正”的正常运动模式（仅占 27%至 71%）。
