Multiclass classifiers for hand-gesture recognition of electromyographic signals from WyoFlex Band
Ulises Villela, Alessandra Grossi, Francesca Gasparini, Iván Salgado, Mariana Ballesteros · 2024
This manuscript analyses the performance of different machine learning models classifying hand gestures from electromyography (EMG) signals. The EMG information is obtained from the WyoFlex armband, a wearable bracelet with four sensors that capture the EMG of the forearm. This study considers four different models, the K-Nearest Neighbor (KNN), Support Vector Machines (SVM), Artificial Neural Networks (ANN), and Long Short-Term Memories (LSTM) for classifying four and six hand gestures. The methodology comprises different scenarios, including the application of the Synthetic Minority Over-Sampling (SMOTE) algorithm to increase the cardinality of the dataset and the Minimum-Redundancy-Maximum-Relevance (MRMR) technique to reduce computational costs. These models are compared considering the classification of four and six different hand gestures in three different scenarios: a) including the SMOTE technique, b) including the MRMR procedure without SMOTE, and c) including both MRMR and SMOTE. The results show an overall accuracy between 79.32% to 94.90% on the four gestures classification and from 75.14% to 92.80% for six gestures classification, with the SVM classifier producing the best performance in training time and accuracy. The application of the MRMR algorithm yields a reduction of the training time in all models applying the K-fold and Leave-One-Subject-Out (LOSO) cross validation methods, the accuracy is not strongly affected reducing the number of features in almost all the models.