Abstract
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.
Highlights
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
We developed machine learning-based classifiers to identify the ten gestures for each subject using artificial neural network (ANN), support vector machine (SVM), random forest (RF), and logistic regression (LR)
We demonstrated the performance of personalized hand/finger gesture classifiers based on TD features only
Summary
Academic Editors: Roberto Merletti and Jesus Lozano. Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Electromyograms (EMGs) have been employed to control prosthetic limbs, such as hands and wrists. EMG signals recorded from specific muscles associated with hand and finger gestures can be used to control a variety of movements. Individual finger (IF) gestures are considered to be more difficult to classify than whole-hand and wrist gestures due to the complexity and subtlety of muscle usage for IF movements [1]. Most finger gesture prediction models rely on EMG signals from a large number of channels, which results in the high cost and complexity of the system [2]
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