An experimental study on upper limb position invariant EMG signal classification based on deep neural network. (January 2020) - Mukhopadhyay, Anand Kumar; Samui, Suman
Highlights: Mainly focused on fully connected deep neural network architecture for the classification of hand movements using surface electromyography (sEMG) signal obtained from the upper limb of different subjects through multi-electrode channels at five different limb positions. In this paper, we strive for minimal signal processing for feature extraction which can be obtained from the time domain while avoiding the feature dimension reduction process and rely on deep learning to automate the process of feature extraction. Furthermore, to the best of our knowledge, our work provides the first step-by-step detailed empirical exploration of deep learning methods applied to EMG classification task. Please note that our objective is not to achieve the state-of-the-art performance, but rather to explore the deep learning framework for EMG classification to the other existing EMG classification schemes whose performance often depends on the complex feature set. Abstract: The classification of surface electromyography (sEMG) signal has an important usage in the man-machine interfaces for proper controlling of prosthetic devices with multiple degrees of freedom. The vital research aspects in this field mainly focus on data acquisition, pre-processing, feature extraction and classification along with their feasibility in practical scenarios regarding implementation and reliability. In this article, we have demonstrated a detailed empirical exploration on Deep Neural Network (DNN) based classification system for the upper limb position invariant myoelectric signal. The classification of eight different hand movements is performed using a fully connected feed-forward DNN model and also compared with the existing machine learning tools. In our analysis, we have used a dataset consisting of the sEMG signals collected from eleven subjects at five different upper limb positions. The time domain power spectral descriptors (TDPSD) is used as the feature set to train the DNN classifier. In contrast to the prior methods, the proposed approach excludes the feature dimensionality reduction step, which in turn significantly reduce the overall complexity. As the EMG signal classification is a subject-specific problem, the DNN model is customized for each subject separately to get the best possible results. Our experimental results in various analysis frameworks demonstrate that DNN based system can outperform the other existing classifiers such as k-Nearest Neighbour (kNN), Random Forest, and Decision Tree. The average accuracy obtained among the five subjects for DNN, SVM, kNN, Random Forest and Decision Tree is 98.88%, 98.66%, 90.64%, 91.78%, and 88.36% respectively. Moreover, it can achieve competitive performance with the state-of-the-art SVM based model, even though the proposed DNN model requires minimal processing in feature engineering. This study provides an insight into the detailed step-by-step empirical procedure to achieve the optimum results regarding classification accuracy using the DNN model.