Smarter Canes: Low-Cost Sensing for Step Counting and Self-Supervised Gait Assessment

Wrist-worn wearables are the dominant paradigm for step counting, but their performance degrades noticeably when used by people with motor impairments. We instrument a standard cane with a low-cost sensing framework to address this and, at the same time, to lay the foundation for a self-supervised gait-quality assessment in which a convolutional autoencoder trained only on normal walking uses reconstruction error as an anomaly score. A downward-facing time-of-flight sensor measures the distance of the cane tip from the ground and provides the reference against which step counting is validated, reaching 92.9% accuracy, while on the same recordings the anomaly detector reaches 98.7% AUPRC under session-level cross-validation, also ordering the types of gait problems by how strongly they deform the signal.