Deep Learning-Based Recognition of Locomotion Mode, Phase, and Phase Progression Using Inertial Measurement Units

Abstract Recently, wearable gait-assist robots have been evolving towards using soft materials designed for the elderly rather than individuals with disabilities, which emphasize modularization, simplification, and weight reduction. Thus, synchronizing the robotic assistive force with that of the user’s leg movements is crucial for usability, which requires accurate recognition of the user’s gait intent. In this study, we propose a deep learning model capable of identifying not only gait mode and gait phase but also phase progression. Utilizing data from five inertial measurement units placed on the body, the proposed two-stage architecture incorporates a bidirectional long short-term memory-based model for robust classification of locomotion modes and phases. Subsequently, phase progression is estimated through 1D convolutional neural network-based regressors, each dedicated to a specific phase. The model was evaluated on a diverse dataset encompassing level walking, stair ascent and descent, and sit-to-stand activities from 10 healthy participants. The results demonstrate its ability to accurately classify locomotion phases and estimate phase progression. Accurate phase progression estimation is essential due to the age-related variability in gait phase durations, particularly evident in older adults, the primary demographic for gait-assist robots. These findings underscore the potential to enhance the assistance, comfort, and safety provided by gait-assist robots.