Electromyogram-Based Classification of Hand and Finger Gestures Using Artificial Neural Networks

Sensors

Ji Young Min¹  Kyung Hyun Lee¹  Sangwon Byun¹ 

[1] Department of Electronics Engineering, Incheon National University, Incheon 22012, Korea;

关键词: electromyogram;    EMG;    machine learning;    physiological signal;    hand-finger movement;    gesture recognition;   

DOI  :  10.3390/s22010225

来源: DOAJ

【 摘 要 】

Electromyogram (EMG) signals have been increasingly used for hand and finger gesture recognition. However, most studies have focused on the wrist and whole-hand gestures and not on individual finger (IF) gestures, which are considered more challenging. In this study, we develop EMG-based hand/finger gesture classifiers based on fixed electrode placement using machine learning methods. Ten healthy subjects performed ten hand/finger gestures, including seven IF gestures. EMG signals were measured from three channels, and six time-domain (TD) features were extracted from each channel. A total of 18 features was used to build personalized classifiers for ten gestures with an artificial neural network (ANN), a support vector machine (SVM), a random forest (RF), and a logistic regression (LR). The ANN, SVM, RF, and LR achieved mean accuracies of 0.940, 0.876, 0.831, and 0.539, respectively. One-way analyses of variance and F-tests showed that the ANN achieved the highest mean accuracy and the lowest inter-subject variance in the accuracy, respectively, suggesting that it was the least affected by individual variability in EMG signals. Using only TD features, we achieved a higher ratio of gestures to channels than other similar studies, suggesting that the proposed method can improve the system usability and reduce the computational burden.

【 授权许可】

Unknown