Adopting BiLSTM for Gait Phase Recognition in Exoskeleton Control via sEMG Signals

Exoskeletons are emerging as powerful tools for lower-limb rehabilitation., providing assistance and gait training capabilities. This study investigates the integration of electromyography (EMG) signals with Deep Learning (DL) techniques to identify gait phases (stance and swing) during exoskeleton-assisted walking. A Bidirectional Long Short-Term Memory is trained on walking overground EMG data collected from 26 healthy subjects and tested on both overground and treadmill walking data. The model's online prediction ability is evaluated using as input the Raw (R) data and two different data pre-processing methods: Root Mean Square Value (RMSV) and Savitzky-Golay (SG) smoothing. The model demonstrates adaptability across different walking conditions and achieves precise identification of stance and swing phase within gait data., with the best overall accuracy of about 90% for walking overground RMSV data and about 92% with walking on treadmill SG data. The study also evaluates the time taken for the prediction of the gait phases., crucial for effective exoskeleton control., estimating the time spent for the pre-processing and the classification procedures. The results are of 69.5 milliseconds for R data., 94 milliseconds for SG data and 101 milliseconds for RMSV data., considering a I-second segment of EMG signal. The SG filter emerges as the preferred methodology as it appears to represent a trade-off between accuracy and processing time. The proposed DL model shows potential for enhancing the precision and responsiveness of exoskeleton-assisted rehabilitation., providing personalized support to individuals undergoing gait training.