A Visual-Inertial Approach to Human Gait Estimation

Ahmed Ahmed; Stergios Roumeliotis

ICRA 2018poster23 citations

A Visual-Inertial Approach to Human Gait Estimation

Abstract

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.

BibTeX

@inproceedings{icra2018_avisualinertiala,
  title = {A Visual-Inertial Approach to Human Gait Estimation},
  author = {Ahmed Ahmed and Stergios Roumeliotis},
  booktitle = {ICRA 2018},
  year = {2018}
}