A Visual-Inertial Approach to Human Gait Estimation
Ahmed Ahmed, Stergios I. Roumeliotis · 2018
This paper addresses the problem of gait estimation using visual and inertial data, as well as human motion models. Specifically, a batch least-squares (BLS) algorithm is presented that fuses data from a minimal set of sensors [two inertial measurement units (IMUs), one on each foot, and a head-mounted IMU-camera pair] along with motion constraints corresponding to the different walking states, to estimate the person's head and feet poses. Subsequently, gait models are employed to solve for the lower-body's posture and generate its animation. Experimental results against the VICON motion capture system demonstrate the accuracy of the proposed minimal sensors-based system for determining a person's motion.