Human Locomotion Implicit Modeling Based Real-Time Gait Phase Estimation

TL;DR

This paper introduces a neural network-based gait phase estimation method using IMU signals, combining temporal convolution and transformer layers, with a novel pre-training strategy to improve accuracy and robustness for exoskeleton control.

Contribution

It proposes an implicit human locomotion modeling approach with a channel-wise masked reconstruction pre-training, enhancing gait phase estimation under various terrain conditions.

Findings

Achieves low gait phase RMSE and phase rate MAE on stable terrain.

Maintains high accuracy during terrain transitions.

Validated on a hip exoskeleton demonstrating reliable gait cycle detection.

Abstract

Gait phase estimation based on inertial measurement unit (IMU) signals facilitates precise adaptation of exoskeletons to individual gait variations. However, challenges remain in achieving high accuracy and robustness, particularly during periods of terrain changes. To address this, we develop a gait phase estimation neural network based on implicit modeling of human locomotion, which combines temporal convolution for feature extraction with transformer layers for multi-channel information fusion. A channel-wise masked reconstruction pre-training strategy is proposed, which first treats gait phase state vectors and IMU signals as joint observations of human locomotion, thus enhancing model generalization. Experimental results demonstrate that the proposed method outperforms existing baseline approaches, achieving a gait phase RMSE of $2.729 \pm 1.071%$ and phase rate MAE of $0.037 \pm 0.016%$ under stable terrain conditions with a look-back window of 2 seconds, and a phase RMSE of $3.215 \pm 1.303%$ and rate MAE of $0.050 \pm 0.023%$ under terrain transitions. Hardware validation on a hip exoskeleton further confirms that the algorithm can reliably identify gait cycles and key events, adapting to various continuous motion scenarios. This research paves the way for more intelligent and adaptive exoskeleton systems, enabling safer and more efficient human-robot interaction across diverse real-world environments.