A contactless and AI-assisted sensor for gait assessment of stroke patients

The gait parameters, i.e., gait speed, stride length, step length, and cadence, serve as key indicators for patients with mobility impairments. Monitoring these parameters can significantly aid in assessing recovery progress, especially in stroke patients. Compared to traditional clinical visits, wearable sensors, or motion capture systems, millimeter-wave (mmWave) device offers unique advantages: low-cost, contactless, light-free, and no privacy invasion, making frequent gait monitoring feasible for home deployment. We introduce mmGait, an mmWave-based system that extracts foot features from reflections and learns walking patterns to accurately predict the 3D locations of both feet, from which we can derive the gait parameters automatically without requiring subjects to wear any sensors or be recorded by cameras. Across multiple stroke patients, mmGait achieves low median errors in estimating key gait parameters: less than 0.03 m/s for gait speed, ∼3 cm for stride length, 7.4 cm for step length, and below 10 steps/min for cadence. Agreement analysis further demonstrates good agreement with the OptiTrack reference for gait speed and stride length, supporting the feasibility of the proposed contactless approach for coarse spatiotemporal gait assessment.