A Modeling of Extended Kalman Filter for Elbow Joint Angle Estimation Based on Electromyography
Triwiyanto Triwiyanto (1), Iswanto Iswanto (2), Bambang Guruh Iranto (1), I Dewa Gede Hari Wisana (1), Moch. Prastawa Assalim Tetra Putra (1)
(1) Department of Electromedical Engineering,
POLTEKKES KEMENKES Surabaya, Indonesia
(2) Department of Electrical Engineering,
Universitas Muhammadiyah Yogyakarta, Indonesia
Abstract
The essential problem in the estimation of a human joint angle based on electromyography (EMG) signal is the non-linearity characteristic of the EMG feature. The non-linearity of the EMG features influence the performance of the estimation. The objective of this paper is to develop an extended Kalman filter model to predict the elbow joint angle based on the EMG signal. The EMG signal is recorded from biceps muscle using disposible electrodes (Ag/AgCl). The recording of the EMG signal was conducted when the subject was instructed to perform a flexion and extension motion. In this study, a periodic and random motion were chosen to examine the proposed method. The EMG signal was extracted using sign slope feature (SSC) to obtain the information which is related to the posisition of the elbow. The response of the features was ploted to get the function of observation state. The extended Kalman filter (EKF) was chosen to linearize and to estimate the elbow joint angle based on EMG features. The performance improvement from KF to EKF based method is 12.81% and 9.65% for periodic and random motion, respectively. We have demonstrated the effectiveness of the proposed method to improve the performance of the estimation, further it can be implemented to an assistive exoskeleton for elderly people or stroke patient for better live
Keywords: Extended Kalman filter, elbow joint angle estimation, EMG, features non- linear.
Topic: International Symposium of Engineering, Technology, and Health Sciences