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October 20, 2025Open Access

Human Locomotion Implicit Modeling Based Real-Time Gait Phase Estimation

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Neural network model improves gait phase estimation accuracy in exoskeletons, suggesting better adaptation during motion transitions.

The proposed model achieves a gait phase RMSE of 2.729% under stable terrain conditions, enhancing accuracy.
With a look-back window of 2 seconds, it can identify key gait events reliably during motion transitions.
The channel-wise masked reconstruction strategy improves model generalization by treating gait phases and IMU signals jointly.
Hardware validation on a hip exoskeleton demonstrates effective identification of continuous motion scenarios for enhanced human-robot interaction.

Ji et al. (2025) studied this question.

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