Differentiating hand gestures from forearm muscle activity using machine learning.

Abstract

This study explored the use of forearm electromyography data to distinguish eight hand gestures. The neural network (NN) and random forest (RF) algorithms were tested on data from 10 participants. As window sizes increase from 200 ms to 1000 ms, the algorithm accuracies increased with RF from 85% to 97% due to the increased temporal resolution. It was also noticed that the RF performed better with an accuracy of 85% than the NN with accuracy 80% when the temporal resolution was smaller, indicating the RF will be efficient when quick-response time is important. As the window size increases, the NN showed higher performance, suggesting that NN will be useful when higher accuracy is required. Future studies should increase the sample size, include more hand gestures, use different feature extraction methods and test different algorithms to improve the accuracy and efficiency of the system.