Comparability of Methods for Assessing Gait Quality Remotely

Advancements in remote gait analysis technologies enable efficient, cost-effective and personalized real-time assessments at home. This study aims to contribute evidence as to the comparability of gait quality metrics of three methods of remote gait assessment in individuals with Parkinson’s Disease (PD): (1) Observation, (2) Wearable sensor, and (3) Pose estimation. A cross-sectional, multiple case series study was conducted remotely. Twenty participants submitted videos performing a modified TUG test with the Heel2ToeTM wearable. Each video was analyzed by six raters using the checklist specific to PD developed for this study and the MediaPipe Pose estimation library. The observational ratings agreed with the Heel2ToeTM on detecting heel strike 64% of the time and 28.5% of the time on detecting push-off. The difference in the ranks of paired observations on the Wilcoxon Signed Rank sum test between the pairs of methods compared was significant for all parameters except for push-off when estimates from MediaPipe were compared to the ratings from the Observational Checklist, W = 86 (p = 0.498). A combination of digital technologies for remote gait analysis, such as wearable sensors and pose estimation, can detect subtle nuances in gait impairments that may be overlooked by the human eye.