Modelling and Estimation of Human Walking Gait for Physical Human-Robot Interaction

TL;DR

This paper presents a real-time model and estimation method for human walking gait using an Extended Kalman Filter, enabling improved physical human-robot interaction through accurate kinematic parameter estimation.

Contribution

It introduces a novel EKF-based approach to estimate human gait parameters from complex walking trajectories, enhancing real-time interaction capabilities.

Findings

Successful estimation of gait parameters from real walking data

Robustness to complex trajectories and changing step frequency

Improved accuracy over heuristic filtering methods

Abstract

An approach to model and estimate human walking kinematics in real-time for Physical Human-Robot Interaction is presented. The human gait velocity along the forward and vertical direction of motion is modelled according to the Yoyo-model. We designed an Extended Kalman Filter (EKF) algorithm to estimate the frequency, bias and trigonometric state of a biased sinusoidal signal, from which the kinematic parameters of the Yoyo-model can be extracted. Quality and robustness of the estimation are improved by opportune filtering based on heuristics. The approach is successfully evaluated on a real dataset of walking humans, including complex trajectories and changing step frequency over time.