Modelling and Estimation of Human Walking Gait for Physical Human-Robot Interaction
This work addresses the need for accurate human motion estimation in robotics applications, but it is incremental as it builds on existing models and filtering techniques.
The paper tackled the problem of real-time estimation of human walking kinematics for physical human-robot interaction by modeling gait velocity with a Yoyo-model and using an Extended Kalman Filter with heuristic filtering, achieving successful evaluation on a real dataset with complex trajectories and varying step frequencies.
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.