Real-Time Neural Network-Based Gait Phase Estimation Using a Robotic Hip Exoskeleton

Lower limb exoskeletons provide assistance during the gait cycle using a state variable, one in particular is gait phase. This is crucial for the exoskeleton controller to provide the user accurate assistance. Conventional methods often utilize an event marker to estimate gait phase by computing the average stride time. However, this strategy has limitations in adapting to dynamic speeds. We developed a sensor fusion-based neural network model to estimate the gait phase in real-time that can adapt to dynamic speeds ranging from 0.6 to 1.1 m/s. Ten able-bodied subjects walked with an exoskeleton using our estimator and were provided with corresponding torque assistance. Our best performing model had RMSE below 29 ms and 4% for real-time estimation and torque generation, respectively, reducing the estimation error by 36.0% (p < 0.01) and torque error by 40.9% (p < 0.001) compared to conventional methods. Our results indicate that creating a general user-independent model and additionally training on user-specific data outperforms the user-specific model and user-independent model. Our study validates the feasibility of using a sensor fusion-based machine learning model to accurately estimate the user's gait phase and improve the controllability of a lower limb exoskeleton.