Multistream CNN-BiLSTM Framework for Enhanced Human Activity Recognition Leveraging Physiological Signal

Human activity recognition (HAR) and classification is one of the most hyped and trending domains in the last decade. HAR involves multiple hit and trial approaches, machine and deep learning have emerged as excellent techniques for analyzing various physiological sensors used to capture human activities. This letter introduce a multistream convolutional neural network-bidirectional long short-term memory (CNN-BiLSTM) framework that works on physiological signals corresponding to different activities, in order to achieve an enhanced HAR system. In this work EMG signals that capture the muscles data during activities are used to classify various activities. We achieve an overall average of 98.06% accuracy in predicting activities. In addition to that we achieve 10%–20% more as compared to benchmark model in similar dataset with less computational time. Further the proposed model demonstrates better and remarkable performance in HAR eight-channel benchmark SOTA dataset.