To Develop an EMG-Based Expert System for the Detection of Wrist Flexion and Extension

Artificial Intelligence and Data Science

This paper presents an electromyography (EMG) based system for classification and detection of wrist movement by carrying four different sets of movements. The experiment involved eight healthy subjects. The subjects were asked to put their hands in a horizontal plane. The subject then flexed their wrist from middle position to maximum flexion position and extended wrist to maximum extension position. The activities were performed in pronated as well as in supinated form. Notch filters and Band-pass filters were used in order to eliminate noise and preserve the features. The low and high cut-off frequency for notch and band-pass filter were 10Hz-80Hz with a sample rate of 512Hz. Discrete Wavelet Transform (DWT) was used to extract the features from EMG signals. Classification was carried out using machine learning techniques namely Decision Tree, Random Forest and Optimum Random Forest. Optimum Random Forest provided a testing accuracy of 89.38%.

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.