Fig 1.
Gait speed analysis framework.
Gait signals classification is traced to the sensor location using LRP for speed relevance to gait.
Table 1.
52 Markers labels.
Fig 2.
52 reflective skin markers were positioned on the subjects through anatomical palpation (see Table 1).
Green markers illustrate the front side, while red markers indicate the back. The markers are numbered according to their order in the spatial domain. EMG signals recorded from probes on the right leg muscles are represented in black (E1–E8).
Fig 3.
MOKKA-Motion Kinematic & Kinetic Analyzer reflective markers and force plates. a: front view, b: side view.
Table 2.
Forces and Moments; N: Number of samples is 1142.
Fig 4.
a) Multi-Source Fusion CNN, b) Hybrid CNN+LSTM network, c) Single CNN, d) Dual CNN, e) Quads CNN.
Fig 5.
Gait 4 speeds classification experiment 1 top performance models Using GRF, EMG, Markers, and F&M Data with (Linear Discriminant Analysis, Quadratic Discriminant Analysis, SVM).
The plot displays a model ROC curve, radar spider chart, and confusion matrices. Top performance for Markers data using SVM Accuracy: F1 Score 94%. a: ROC Curve, b: Spider (Radar) Chart, c: Confusion Matrix.
Fig 6.
Gait 4 speeds classification experiment 3 top performance model Using GRF, EMG, Markers, and F&M Data with (Linear Discriminant Analysis, Quadratic Discriminant Analysis, SVM).
The plot displays a model ROC curve, radar spider chart, and confusion matrices. a: ROC Curve, b: Spider (Radar) Chart, c: Confusion Matrix.
Table 3.
Classification F1-Score to Classify Gait Speed.
Fig 7.
Gait 4 speeds classification with Using EMG Data and Quads CNN top performance model experiment 1.
(a) Accuracy across folds, (b) model loss across folds, (c) ROC curve, (d) Matthews Correlation Coefficient (MCC) across folds, (e) Confusion matrix and (f) Normalized confusion matrix.
Fig 8.
Gait 4 speeds classification with Using Marker Data and Dual CNN, precision, recall, and f1-score 97% top performance model experiment 1.
(a) The model accuracy, (b) The model training loss, (c) ROC for Gait classification, (d) ROC for multi-class Gait, (e) The confusion matrix, and (f) Normalized confusion matrix.
Fig 9.
F1 scores for all models based on the data type and experiment 1 to 3.
Fig 10.
Heatmap values t-test and P-values between models: 1. SVM, 2. LDA, 3. QDA, 4. Single CNN, 5. Dual CNN, 6. Quads CNN, 7. Multi-Source Fusion CNN, 8. Hybrid CNN+LSTM, 9. Temporal CNN (TCN), 10. Transformer, 11. GRU.
a, b, c: GRF experiment 1, 2, 3 respectively. d, e, f: EMG experiment 1, 2, 3 respectively. g, h, i: Markers experiment 1, 2, 3 respectively. j, k, l: F&M experiment 1, 2, 3 respectively.
Fig 11.
Heatmap values paired t-test and P-values between experiments.
For models: 1. SVM, 2. LDA, 3. QDA, 4. Single CNN, 5. Dual CNN, 6. Quads CNN, 7. Multi-Source Fusion CNN, 8. Hybrid CNN+LSTM, 9. Temporal CNN (TCN), 10. Transformer, 11. GRU. a: GRF, b: EMG, c: Markers, d: F&M.
Fig 12.
Comparison of XAI Analyzers on Markers: Input, deep Taylor bounded, Deep Taylor, LRP alpha 2 beta 1, LRP alpha 2 beta 1, LRP-Z, LRP-Epsilon, sequential preset a flat, gradient, deconvnet, guided backprop. a: heatmap, b: line plot.
Fig 13.
Comparison of CNN models of experiment 1 perturbation over 100 steps for Single CNN, Dual CNN, Quads CNN for Markers, EMG, GRF, and F&M datasets. a: heatmap, b: line plot.
With Quads CNN models using Markers data maintain robust performance in the heatmap, b: line plot.
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.
Fig 15.
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 2 (moderate (0.8–1.2 m/s) or self-selected natural speed), and panels b, d, f corresponds to Class 3 (fast preferred speed). For Class 2, the most influential features are: Left posterior calcaneus coordinates, left 1st metatarsal head coordinates, Left 2nd metatarsal head coordinates. For Class 3, the most influential features are: Left and Right lateral tibial malleolus coordinates, Left and Right medial tibial malleolus coordinates.