A fuzzy inference system for hand injury level classification using surface electromyography signals

Abstract

The surface electromyography (SEMG) is extensively used in assessing injuries in the musculoskeletal parts of the body. Integrating intelligence in such applications impacted the development of intelligent medical devices. The conventional way of assessing hand injury level is manually and subjectively done by experts to identify the type of rehabilitation program recommended to the patient. This work uses SEMG data to classify hand injury levels through a fuzzy inference system (FIS). Three of the many features of the SEMG signal were selected based on its high distinction levels, namely, the root-mean-square, enhanced mean-absolute value, and the waveform length. Segmentation through a sliding window method is used for feature extraction. The FIS rules were designed based on the assessment guide of the experts. A Mamdani-type FIS classifier was used with membership functions which are a combination of trapezoidal and triangular types. A MATLAB Simulink model was also designed to test the FIS system. The setup effectively identified injury levels through tests with a healthy subject, wherein no muscle activation means an injury, while the full fist, as a full muscle activation or healthy. In between signal values vary with different injury levels. In the future, this setup will be tested on patients in a rehabilitation clinic for validation.