Artificial Intelligence for sEMG-Based Muscular Movement Recognition for Hand Prosthesis

作     者：Hye, Nafe Muhtasim Hany, Umma Chakravarty, Sumit Akter, Lutfa Ahmed, Imtiaz 

作者机构：Ahsanullah Univ Sci & Technol Dept Elect & Elect Engn Dhaka 1208 Bangladesh Kennesaw State Univ Dept Elect & Comp Engn Marietta GA 30060 USA Bangladesh Univ Engn & Technol Dept Elect & Elect Engn Dhaka 1205 Bangladesh Howard Univ Dept Elect Engn & Comp Sci Washington DC 20059 USA 

出 版 物：《IEEE ACCESS》 (IEEE Access)

年 卷 期：2023年第11卷

页      面：38850-38863页

核心收录：

基　　金：Department of Electrical Engineering and Computer Science  Howard University  Washington  DC  USA 

主　　题：Feature extraction Databases Electrodes Electromyography Real-time systems Prosthetic hand Gesture recognition computational and artificial intelligence biomedical signal processing 

摘      要：The muscular activities gathered by real-time myoelectric interfaces of surface electromyography (sEMG) can be used to develop myoelectric prosthetic hands for physically disabled people. However, the acquired myoelectric signals must be accurately classified in real time to properly control the operation of the external devices. In this study, we propose methods for detecting and classifying muscular activities using sEMG signals. These methods include outlier removal, data manipulation, data preprocessing, dimensionality reduction, and classification. We use the Ninapro database 1 (DB1) containing sEMG signals from 27 intact subjects while performing 53 hand movements repeatedly. We apply the Principle Component Analysis (PCA), Independent Component Analysis (ICA), and t-distributed Stochastic Neighbor Embedding (t-SNE) feature extraction methods for dimensionality reduction. Five machine learning (ML) algorithms and deep learning artificial neural networks (ANN) are applied for the classification of muscular movements. It is observed that for the recognition of 53 muscular movements of 27 subjects with preprocessed raw data, ANN obtains the highest accuracy of 93.92% for inter-subject and 97.73% for intra-subject movement recognition. Among the ML algorithms, K-Nearest Neighbors (KNN) performs the best with both t-SNE features and the preprocessed raw data in least computational time. With the preprocessed raw data, KNN obtains 93.174% and 97.458% for inter-subject and intra-subject movement classification, respectively while with the t-SNE features, KNN obtains 89.844% accuracy for inter-subject and 95.04% accuracy for intra-subject in reduced computational time.