Bi-Directional Long Short-Term Memory-Based Gait Phase Recognition Method Robust to Directional Variations in Subject’s Gait Progression Using Wearable Inertial Sensor

• Published in: Sensors (Basel, Switzerland) Vol. 24; no. 4; p. 1276
• Main Authors: Jeon, Haneul, Lee, Donghun
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 17.02.2024; MDPI
• Subjects: Accuracy; Algorithms; bi-directional LSTM; Biomechanics; Calibration; Data collection; Electromyography; Fitness equipment; Gait; gait phase recognition; Human body; Humans; Information management; Labeling; Methods; Neural Networks, Computer; Posture; Pressure distribution; Reproducibility; Reproducibility of Results; Sensors; sliding window; time-series data; Walking; Wearable Electronic Devices; wearable sensor; time-series data; wearable sensor; sliding window; bi-directional LSTM; gait phase recognition
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s24041276

Inertial Measurement Unit (IMU) sensor-based gait phase recognition is widely used in medical and biomechanics fields requiring gait data analysis. However, there are several limitations due to the low reproducibility of IMU sensor attachment and the sensor outputs relative to a fixed reference frame. The prediction algorithm may malfunction when the user changes their walking direction. In this paper, we propose a gait phase recognition method robust to user body movements based on a floating body-fixed frame (FBF) and bi-directional long short-term memory (bi-LSTM). Data from four IMU sensors attached to the shanks and feet on both legs of three subjects, collected via the FBF method, are processed through preprocessing and the sliding window label overlapping method before inputting into the bi-LSTM for training. To improve the model’s recognition accuracy, we selected parameters that influence both training and test accuracy. We conducted a sensitivity analysis using a level average analysis of the Taguchi method to identify the optimal combination of parameters. The model, trained with optimal parameters, was validated on a new subject, achieving a high test accuracy of 86.43%.