Simultaneous Recognition of Locomotion Mode, Phase, and Phase Progression Using Deep Learning Models

Despite advances in gait-assist wearable robots, application in real-world scenarios remains limited, largely due to challenges in developing an effective user intention recognition algorithm. These algorithms are crucial as they enable the robot to move harmoniously with the user by predicting their intent during various locomotion activities such as level walking, stair ascent, stair descent, and sit-to-stand. It is essential to not only identify these locomotion modes but also their phases and progression for real-time assistance. Traditional classification methods often require extensive manual feature extraction from signals like those from inertial measurement units (IMU), electromyography, and plantar force sensors. Recent machine learning, particularly deep learning approaches, have simplified this process through automatic feature extraction. However, no existing method simultaneously predicts locomotion modes, phases, and phase progression, which is significant for personalized assistance. This study introduces a deep learning framework that classifies locomotion modes and phases and estimates the phase progressions using IMU data from sensors placed on the sternum and limbs. Results from five participants show that our model effectively classifies the locomotion phase and well estimates the phase progression percentage. The model was evaluated using a leave-one-subject-out approach, ensuring generalizability across different users.