A Real-Time Gait Phase Detection Method Based on BiLSTM-Attention Model

Real-time gait phase detection is essential to achieve accurate and stable walking assistance in intelligent rehabilitation training for patients with motor disorders. This study proposed an efficient real-time detection method to detect three gait phases (loading response, stance, and swing) based on a bidirectional long short-term memory network with an attention layer (BiLSTM-Attention). We validated our method on a public dataset where eight healthy subjects' data during treadmill walking were employed. A single inertial measurement unit (IMU) was attached to the shank to measure the sagittal plane acceleration of the lower leg and the angular velocity around the central lateral axis. These data were transposed and segmented into data sequences based on labels using a sliding window method. The data from 8 participants were divided into the training, validation, and test sets (5:1:2). Results showed the average recognition accuracy of the proposed model on new subjects was 97.40% with an average time delay of 15.7±10.1ms, showing the method's potential to be applied for practice use.