A Minimalist, Foot-Mounted IMU Approach to Parkinson's Disease Detection in Semi-Controlled Settings

Scalable tools for Parkinson's disease (PD) detection are needed beyond laboratory-bound assessments. We present and evaluate a deliberately minimalist approach: a single six-axis IMU per shoe coupled with a lightweight 1D-CNN, tested during a six-minute walk test (6MWT) conducted indoors and outdoors under semi-controlled conditions. Eighty-nine participants (44 PD; 45 healthy controls) were instrumented bilaterally at 50 Hz. Signals (accelerometer and gyroscope) were standardized to a common shoe-referenced frame and ingested as raw time series using 0.5-s non-overlapping windows. The network used three tapered convolutional blocks (32-16-8 filters) with global average pooling (2,818 parameters). Evaluation employed participant-level, stratified 5-fold cross-validation repeated across seeds. At the foot level, overall accuracy was 80.58% (sensitivity 74.76%, specificity 85.55%). Applying a clinically motivated one-foot-positive criterion (OFPC), classifying a participant as PD if either foot was positive, improved patient-level performance to 82.1% accuracy, with 84.78% sensitivity and 80.29 specificity. Indoor performance (accuracy 81.48%, sensitivity 91.89%, specificity 72.73%,) exceeded outdoor sensitivity, whereas outdoor testing maintained higher specificity (accuracy 82.17%, sensitivity 80%, specificity 83.91%), reflecting expected context variability. Compact architecture and short, non-overlapping windows minimize memory, computation and latency, supporting on-device deployment on resource-constrained wearables. These results indicate that a foot-mounted, single-modality IMU set-up combined with a compact CNN can deliver clinically meaningful PD discrimination under ecologically relevant conditions, providing a practical pathway toward real-time case identification and follow-up in routine care while preserving scalability and usability.