A DNN-Based Learning Framework for Continuous Movements Segmentation

This study presents a novel experimental paradigm for collecting Electromyography (EMG) data from continuous movement sequences and a Deep Neural Network (DNN) learning framework for segmenting movements from these signals. Unlike prior research focusing on individual movements, this approach characterizes human motion as continuous sequences. The DNN framework comprises a segmentation module for time point level labeling of EMG data and a transfer module predicting movement transition time points. These outputs are integrated based on defined rules. Experimental results reveal an impressive capacity to accurately segment movements, evidenced by segmentation metrics (accuracy: \(88.3\%\) ; Dice coefficient: \(82.9\%\) ; mIoU: \(72.7\%\) ). This innovative approach to time point level analysis of continuous movement sequences via EMG signals offers promising implications for future studies of human motor functions and the advancement of human-machine interaction systems.