Online tracking of the lower body joint angles using IMUs for gait rehabilitation

Abstract

An important field in physiotherapy is the rehabilitation of gait. A continuous assessment and progress tracking of a patient's ability to walk is of clinical interest. Unfortunately the tools available to the therapists are very time-consuming and subjective. Non-intrusive, small, wearable, wireless sensors can be worn by the patients and provide inertial measurements to estimate the pose of the lower body during walking. For this purpose, we propose two different kinematic models of the human lower body. We use an Extended Kalman Filter to estimate the joint angles and show that a variety of sensors, such as accelerometers, gyroscopes, and motion capture markers, can be used and fused together to aid the joint angle estimate. The algorithm is validated on gait data collected from healthy participants.