Explaining machine learning models for age classification in human gait analysis

TL;DR

This study applies Layer-wise Relevance Propagation to CNN-based gait analysis for age classification, revealing which features influence predictions and highlighting the model's strengths and limitations across age groups.

Contribution

It demonstrates the use of LRP for explaining CNN decisions in gait-based age classification, enhancing interpretability of ML models in biomechanics.

Findings

CNN achieved 60.1% accuracy in age classification.

LRP identified key gait features influencing predictions.

Model distinguished young and old adults well, less so middle-aged.

Abstract

Machine learning (ML) models have proven effective in classifying gait analysis data, e.g., binary classification of young vs. older adults. ML models, however, lack in providing human understandable explanations for their predictions. This "black-box" behavior impedes the understanding of which input features the model predictions are based on. We investigated an Explainable Artificial Intelligence method, i.e., Layer-wise Relevance Propagation (LRP), for gait analysis data. The research question was: Which input features are used by ML models to classify age-related differences in walking patterns? We utilized a subset of the AIST Gait Database 2019 containing five bilateral ground reaction force (GRF) recordings per person during barefoot walking of healthy participants. Each input signal was min-max normalized before concatenation and fed into a Convolutional Neural Network (CNN). Participants were divided into three age groups: young (20-39 years), middle-aged (40-64 years), and older (65-79 years) adults. The classification accuracy and relevance scores (derived using LRP) were averaged over a stratified ten-fold cross-validation. The mean classification accuracy of 60.1% was clearly higher than the zero-rule baseline of 37.3%. The confusion matrix shows that the CNN distinguished younger and older adults well, but had difficulty modeling the middle-aged adults.