Classification of Hand Movements Using MYO Armband on an Embedded Platform

Abstract

The study proposed the classification and recognition of hand gestures using electromyography (EMG) signals for controlling the upper limb prosthesis. In this research, the EMG signals were measured through an embedded system by wearing a band of MYO gesture control. In order to observe the behavior of these change movements, the EMG data was acquired from 10 healthy subjects (five male and five females) performing four upper limb movements. After extracting EMG data from MYO, the supervised classification approach was applied to recognize the different hand movements. The classification was performed with a 5-fold cross-validation technique under the supervision of Quadratic discriminant analysis (QDA), support vector machine (SVM), random forest, gradient boosted, ensemble (bagged tree), and ensemble (subspace K-Nearest Neighbors) classifier. The execution of these classifiers shows the overall accuracy of 83.9% in the case of ensemble (bagged tree) which is higher than other classifiers. Additionally, in this research an embedded system-based classification approach of hand movement was used for designing an upper limb prosthesis. This approach is different than previous techniques as MYO is used with an external Bluetooth module and different libraries that make its movement and performance boundless. The results of this study also inferred the operations which were easy for hand recognition and can be used for developing a powerful, efficient, and flexible prosthetic design in the future.