Gait phase recognition method for lower limb exoskeleton robot based on SE channel attention mechanism enhanced TCN-SVM

This study develops an integrated system for collecting kinematic signals from lower limb exoskeletons, combining thigh muscle pressure with inertial measurements. The system captures muscle pressure, triaxial acceleration, and angle data. A temporal convolutional network model with an SE attention mechanism and SVM classifier is proposed for gait phase recognition. Results show that the FMG-IMU data fusion strategy achieves high accuracy, stability, and low sensitivity to external noise, effectively recognizing gait phases and improving exoskeleton performance.