Deep Learning-Based Parkinson's Disease Classification Using RGB Plantar Pressure Gait Images: A Comparative Study of CNN and Transformer Architectures.

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Background/Objectives: Parkinson's disease (PD) is associated with gait abnormalities that provide quantitative information on motor function. This study developed and evaluated a signal-to-image deep learning framework for distinguishing participants with diagnosed PD from healthy controls using plantar pressure gait signals. Methods: VGRF signals from the PhysioNet Gait in Parkinson's Disease Database were transformed into RGB images encoding left-foot pressure, right-foot pressure, and the absolute bilateral difference. ResNet50, EfficientNet-B0, ViT-Tiny, and Swin-Tiny were evaluated at three resolutions and compared with 1D-CNN and BiLSTM baselines. Repeated subject-level five-fold cross-validation with three repetitions and participant-level bootstrap analysis were performed. Results: Among RGB models, ViT-Tiny at 384 × 384 achieved a mean accuracy of 77.64%, F1-score of 82.82%, and AUC of 86.40%. In the Swin-Tiny 384 × 384 ablation, the absolute bilateral-difference representation achieved the highest mean AUC (86.68%). However, its participant-level AUC advantage over RGB was not statistically conclusive (ΔAUC = 0.030, 95% CI: -0.011 to 0.068). Conclusions: Signal-derived gait images provide a feasible approach for PD classification, with bilateral-difference information showing potential discriminative value.

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