Linearized Gait Phase Estimation Using BiLSTM

This paper presents a novel deep learning-based approach to address time-delay challenges arising from nonlinear transformations in Gait Phase Estimation, a critical factor in analyzing human gait for lower limb exoskeleton robots. The proposed model leverages hip joint angles and angular velocities as feature data, organized using a sliding window technique, to predict the Linearized Gait Phase (LGP)‖a continuous repre-sentation of gait progression between Heel Strikes. Motivated by the need for real-time and precise gait phase estimation to enhance exoskeleton control, a Bidirectional LSTM network was employed to reduce delays and improve accuracy. The model's effectiveness was validated through comprehensive performance evaluations using metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Square Error (MSE), and R2 score, alongside a temporal analysis of delay reduction. The results demonstrated superior performance, with an MAE of 2.8±0.8%, RMSE of 3.9±1.2%, MSE of 0.1±0.1%, and an R2 score of 98.1±1.8%. Particularly, temporal analysis revealed a marked improvement, achieving an MAE of 0%, compared to 2.74±0.92% reported in prior studies. These findings underscore the proposed model's potential for real-time gait phase estimation, offering significant implications for enhancing the responsiveness and adaptability of lower limb exoskeleton robots.