Enhanced Gait Phase Estimation for Lower Limb Exoskeletons by Integrating Contralateral Leg Information

The estimation of human gait phase has recently been used to parameterize continuous lower limb exoskeleton controllers. By deriving the gait phase estimation directly from the user's biological signals (i.e., joint/segment angles), the user can be granted an avenue to control their progression through the gait cycle. The inherent challenge for this strategy is the high accuracy required, as the estimated gait phase determines the timing of the device's assistive and resistive torques. Poor device timing can result in desynchronization of the device with the user. To address this challenge, this paper presents a novel method for using the information from the contralateral leg to improve the estimation accuracy. Simulation results showed a 75.2% reduction in estimation error relative to the state of the art method for able-bodied individuals. Additionally, an average of 47.6% reduction in estimation error was observed among data from three hemiparetic stroke patients. Across both able bodied and hemiparetic subjects, an estimation RMSE of 2.28% was shown. The evaluation of this technique displays its promise for the control of rehabilitative robotics.Clinical Relevance-This improvement to lower limb exoskeleton control aims to encourage greater user participation in robot assisted gait training for stroke rehabilitation.