EMG-Based Hand Gesture Dataset to Control Electronic Wheelchair for SCI Patients
Shahida Afrin, Hasan Mahmud, Md. Kamrul Hasan · 2022
This paper presents electromyography (EMG)-based hand gesture dataset to control electric wheelchair for the patient with spinal cord injury (SCI). We have recorded eight-channel surface EMG (sEMG) signals from EMG sensor placed at the forearm of the SCI patient. These signals were collected from six hand gesture-based wheelchair control movements (forward, backward, left, right, start and stop). We collected hand gesture data containing different EMG signals from 12 healthy subjects and 7 SCI subjects. Later on, The EMG signals were segmented and the time-domain feature extraction technique was applied to generate 18000 training samples and 10500 testing samples. We then classified the hand gestural EMG signals using 5 different classical machine learning models. We analyze the classification results in two ways. The first one is, training the models using only data of healthy subjects and cross-validated using data from 7 SCI patients. And the second one is by including six SCI patient’s data in the training process along with healthy subjects we performed leave one out cross-validation. From this analysis we were able to achieve highest 95.42% accuracy using decision tree (DT) and Random Forest(RF) algorithms.