Abstract
Exoskeleton assistance has the potential to address many gait related symptoms of Parkinson's disease (PD). However, gait variability, a hallmark of PD, makes designing exoskeleton controllers uniquely challenging. We sought to overcome the challenges that gait variability in PD poses for state estimation by employing machine-learning models for gait-phase estimation within our exoskeleton controller. Using machine-learning-based gait-phase models deployed on a hip exoskeleton (N = 7), we performed a 2-day protocol for people with PD where the first day focused on acclimation to the device and the second focused on evaluating the device by collecting gait metrics. Using 2-min walking tests, we assessed the impact of two different types of fixed torque assistance profiles on spatiotemporal and kinematic gait metrics. We demonstrated significant improvements to hip range-of-motion (8.4%), swing time (4.7%), and peak toe clearance (12.3%) in people with PD when walking with a combined flexion and extension assistance profile as compared to walking without an exoskeleton. Although we saw trends, there were no significant differences from providing only flexion assistance given our sample size. We also demonstrated that participant-specific models reduced gait-phase estimation error by 40%, however, resulting gait metrics were not significantly altered compared to metrics when walking with the generic model. These results demonstrate that ML gait-phase-based control approaches with limited PD-specific data can improve PD gait kinematics, with enhanced accuracy associated with participant-specific data. Ultimately, these results contribute to the goal of assistive exoskeletons in everyday use for people with Parkinson's disease.