A Calibration Method for Gait Analysis with a Single Inertial Sensor in Telerehabilitation

Gait training is essential in rehabilitation as it promotes mobility, prevents falls, and increases independence in daily activities. However, accurate gait assessment in the context of telemedicine remains a challenge. Wearable inertial devices offer value by providing a minimally invasive means to accurately capture movement. This study aims to improve telemedicine-based gait analysis by introducing a method utilizing a class II-B medical device equipped with a chest-worn inertial sensor. To enhance the quality of acceleration signals, we designed and 3D printed an 18-face polyhedron for stationary calibration. We validated this method by comparing its performance to a marker-based motion capture system in a cohort of young healthy adults. The results showed an 82% reduction in the root mean square error (RMSE) of acceleration signals. Furthermore, the study showed statistical and practical significant improvements in spatial gait characteristics. Specifically, there was a 14% reduction in root mean square error for gait velocity, a 59% reduction in RMSE for step length, and a 53% reduction in RMSE for stride length. These improvements in the accuracy of spatial gait feature detection can enhance telerehabilitation and ultimately lead to improved patient outcomes.