Lateral Walking Gait Recognition and Hip Angle Prediction Using a Dual-Task Learning Framework.

ABSTRACT: Lateral walking exercise is beneficial for the hip abductor enhancement. Accurate gait recognition and continuous hip joint angle prediction are essential for the control of exoskeletons. We propose a dual-task learning framework, the "Twin Brother" model, which fuses convolutional neural network (CNN), long short-term memory (LSTM), neural networks (NNs), and the squeezing-elicited attention mechanism to classify the lateral gait stage and estimate the hip angle from electromyography (EMG) signals. The EMG signals of 6 muscles from 10 subjects during lateral walking were collected. Four gait phases were recognized, and the hip angles of both legs were continuously estimated. The sliding window length of 250 ms and the sliding increment of 3 ms were determined by the requirements of respon

SUBMITTER: Luo M 

PROVIDER: S-EPMC12044219 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature