Institute of Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada.
Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, Ontario, Canada.
PLoS One. 2024 Apr 2;19(4):e0300447. doi: 10.1371/journal.pone.0300447. eCollection 2024.
Quantitative gait analysis is important for understanding the non-typical walking patterns associated with mobility impairments. Conventional linear statistical methods and machine learning (ML) models are commonly used to assess gait performance and related changes in the gait parameters. Nonetheless, explainable machine learning provides an alternative technique for distinguishing the significant and influential gait changes stemming from a given intervention. The goal of this work was to demonstrate the use of explainable ML models in gait analysis for prosthetic rehabilitation in both population- and sample-based interpretability analyses. Models were developed to classify amputee gait with two types of prosthetic knee joints. Sagittal plane gait patterns of 21 individuals with unilateral transfemoral amputations were video-recorded and 19 spatiotemporal and kinematic gait parameters were extracted and included in the models. Four ML models-logistic regression, support vector machine, random forest, and LightGBM-were assessed and tested for accuracy and precision. The Shapley Additive exPlanations (SHAP) framework was applied to examine global and local interpretability. Random Forest yielded the highest classification accuracy (98.3%). The SHAP framework quantified the level of influence of each gait parameter in the models where knee flexion-related parameters were found the most influential factors in yielding the outcomes of the models. The sample-based explainable ML provided additional insights over the population-based analyses, including an understanding of the effect of the knee type on the walking style of a specific sample, and whether or not it agreed with global interpretations. It was concluded that explainable ML models can be powerful tools for the assessment of gait-related clinical interventions, revealing important parameters that may be overlooked using conventional statistical methods.
定量步态分析对于理解与移动障碍相关的非典型步态模式非常重要。传统的线性统计方法和机器学习 (ML) 模型通常用于评估步态表现和相关的步态参数变化。然而，可解释性机器学习提供了一种区分给定干预措施引起的显著和有影响力的步态变化的替代技术。这项工作的目的是展示可解释性机器学习模型在假肢康复中的步态分析中的应用，包括基于人群和基于样本的可解释性分析。该模型用于对两种类型的假肢膝关节的截肢者步态进行分类。对 21 名单侧股骨截肢者的矢状面步态模式进行了视频记录，并提取了 19 个时空和运动学步态参数并包含在模型中。评估和测试了四种 ML 模型——逻辑回归、支持向量机、随机森林和 LightGBM——的准确性和精密度。应用 Shapley Additive exPlanations (SHAP) 框架来检查全局和局部可解释性。随机森林产生了最高的分类准确性 (98.3%)。SHAP 框架量化了每个步态参数在模型中的影响程度，其中与膝关节弯曲相关的参数被发现是产生模型结果的最主要影响因素。基于样本的可解释性 ML 提供了基于人群分析的额外见解，包括了解膝关节类型对特定样本行走方式的影响，以及它是否与全局解释一致。研究得出结论，可解释性 ML 模型可以成为评估与步态相关的临床干预措施的有力工具，揭示了使用传统统计方法可能会忽略的重要参数。
