Explainable AI for gait speed analysis from multimodal data fusion
Fig 14
Explainability analysis of the top Quads CNN model from Experiment 2 using LRP (Layer-wise Relevance Propagation) on input samples.
(a & b) Top row: original input signals; bottom row: feature relevance maps from LRP. (c & d) Input samples visualized as heatmaps over body segment reflective markers (as defined in Fig 2 and Table 1). (e & f) Corresponding feature relevance heatmaps mapped onto body segment markers. Panels a, c, e corresponds to Class 0 (very slow gait, 0–0.4 m/s), and panels b, d, f corresponds to Class 1 (slow gait, 0.4–0.8 m/s). For Class 0, the most influential features are: Right acromial tip, Right spine root, and right acromial angle coordinates. For Class 1, the most influential features are: Left acromial tip, left spine root, and left acromial angle coordinates.