Machines Assisting Recovery From Stroke

Locomotor disability remains a major obstacle to community function in stroke survivors. This disability is best characterized by the reduced gait speed and enhanced risk of falls that is observed in the majority of stroke survivors. Current robotic systems have focused on repetitive stepping in constrained, less-challenging environments than overground training and have failed to produce results that can justify their use. In contrast to this approach, this study will use a combinatorial approach on a moving platform (KineAssist-Mobility Activity Center) that simulates and enhances the challenges of overground training. We focus on five critical factors that contribute to reduced speed and fall risk during mobility activities: 1) lower limb weakness; 2) slow lower limb movements; 3) reduced balance; 4) reduced ability to respond to challenges during walking; and 5) reduced aerobic capacity. The end product of this study is to develop a comprehensive and standardized system for assessing and prescribing specific training modalities that can be used by clinicians to help stroke survivors who are limited by slow walking speed and high fall risk, and can improve participation in mobility activities.