Emotion Recognition with Forearm-based Electromyography
This addresses the problem of limited emotional expression in human-computer interaction for applications like gaming and e-learning, though it is incremental as it builds on existing EMG and classification methods.
The paper tackled emotion recognition by classifying forearm-based electromyography signals into relaxed and angry states using a Support Vector Machine, achieving an accuracy of 88.1% after 300 iterations.
Electromyography is an unexplored field of study when it comes to alternate input modality while interacting with a computer. However, to make computers understand human emotions is pivotal in the area of human-computer interaction and in assistive technology. Traditional input devices used currently have limitations and restrictions when it comes to express human emotions. The applications regarding computers and emotions are vast. In this paper we analyze EMG signals recorded from a low cost MyoSensor and classify them into two classes - Relaxed and Angry. In order to perform this classification we have created a dataset collected from 10 users, extracted 8 significant features and classified them using Support Vector Machine algorithm. We show uniquely that forearm-based EMG signal can express emotions. Experimental results show an accuracy of 88.1% after 300 iterations.This shows significant opportunities in various fields of computer science such as gaming and e-learning tools where EMG signals can be used to detect human emotions and make the system provide feedback based on it. We discuss further applications of the method that seeks to expand the range of human-computer interaction beyond the button box.