Clinical and Demographic Factors Influencing the Asymmetry of Gait in Lower-Limb Prosthetic Users

Dąbrowski, Mikołaj; Łysakowska, Katarzyna; Gramala, Adam; Kubaszewski, Łukasz

doi:10.3390/sym14091910

Cited by 1 publication

(2 citation statements)

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“…SVM [ 2 ] is a simple but powerful ML modelling approach for both linear (and nonlinear) classification and regression purposes. In the case of linear separable binary classification problems, a hyperplane separates two different classes.…”

Section: Methodsmentioning

confidence: 99%

“…Clinicians commonly use gait indicators or parameters—such as the step length, stride velocity, and joint angles—to diagnose gait issues and establish a suitable course of rehabilitation [ 1 ]. Gait metrics also play an important role in research, facilitating the assessment of interventions that are most effective in improving gait [ 2 , 3 ]. Gait parameters are typically obtained via instrumented gait analysis techniques, such as optical cameras calibrated to track the body in three-dimensional space or wearable motion sensors (i.e.…”

Section: Introductionmentioning

confidence: 99%

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Exploration and demonstration of explainable machine learning models in prosthetic rehabilitation-based gait analysis

Aghababa,

Andrysek

2024

PLoS ONE

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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.

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Section: Methodsmentioning

confidence: 99%