Adaptive Filtering of Accelerometer and Electromyography Signals Using Extended Kalman Filter for Chewing Muscle Activities

Abstract

Today Electromyography (EMG) and accelerometer (MEMS) based signals can be used in the clinical diagnosis of physical states of muscle activities such as fatigue, muscle weakness, pain, and tremors and in external or wearable robotic exoskeletal systems used in rehabilitation areas. During the recording of these signals taken from the skin surface through non-invasive processes, analysis of the signal becomes difficult due to the electrodes attached to the skin not fully contacting, involuntary body movements, and noises from peripheral muscles. In addition, parameters such as age and skin structure of the subjects can also affect the signal. Considering these negative factors, a new adaptive method based on Extended Kalman Filtering (EKF) model for more effective filtering of the muscle signals based on both EMG and MEMS is proposed in this study. Moreover, the accuracy of the parametric values determined by the filter automatically according to the most effective time and frequency features that represent noisy and filtered signals was determined by different machine learning and classification algorithms. It was verified that the filter performs adaptive filtering with 100% effectiveness with Linear Discriminant.