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

Abstract

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%.

Novel Methods for Personalized Gait Assistance: Three-Dimensional Trajectory Prediction Based on Regression and LSTM Models

“…Su et al [ 17 ] explored Long Short-Term Memory (LSTM)-based models for predicting gait phases and trajectories, achieving similar accuracy in predictions of angular velocities 100–200 ms in advance for thigh, shank, and foot segments using IMU data from 12 subjects walking at five different speeds. Additionally, Haneul et al [ 20 ] examined gait phase recognition using a bi-directional LSTM, utilizing IMU data from sensors on the shank and feet of three subjects, achieving an accuracy of 86.43%. This growing interest in the development of more accurate gait pattern generators recognizes the need for tools which can provide not only robust baseline estimates of gait patterns but also dynamically adapt to the changing conditions of the individual during walking.…”