Our research aims to develop high-performance wearable control systems to enable mobility and improve quality of life for persons with disabilities. Estimates indicate that by 2050 the U.S. will incur a two-fold increase in the incidence of amputation and stroke, due largely to the prevalence of vascular disease. These disabilities severely limit mobility and social activity for millions of Americans, whose ambulation is slower, less stable, and less efficient than that of able-bodied persons. The projected increase in mobility-related disabilities therefore presents a grand challenge to the American workforce and healthcare system. Recent robotic prostheses and orthoses have the potential to restore mobility in impaired populations, but critical barriers in control technology currently limit their performance and practicality.
Current powered legs independently control different joints and time periods of the gait cycle, requiring clinicians to spend significant amounts of time tuning each control model to the individual, and risking falls when environmental perturbations trigger the wrong control model at the wrong time. These limitations are a consequence of the current paradigm for viewing gait patterns as functions of time. However, recent bipedal robots can stably walk, run, and climb stairs with one control model that drives joint patterns as functions of a single mechanical variable, which continuously represents the robot's progression through the gait cycle, i.e., a sense of phase.
Our research attempts to leverage these breakthroughs to transform prosthetic and orthotic technology with a paradigm shift in how the human gait cycle is viewed: as a function of a phase variable rather than time. This work will enable the design of wearable robots with a single control model that measures a biologically-inspired phase variable to match the human's volitional movement and respond to perturbations. Central to this challenge is a fundamental gap in knowledge between disciplines about how the human neuromuscular system might maintain a sense of phase and subsequently control locomotion. Our current research aims to address this gap by 1) identifying biomechanical phase variables used in human locomotion, and 2) designing and experimentally validating phase-based control models on robotic prostheses and orthoses. Through this needs-driven work we hope to establish a new field of inquiry at the scientific interface between robot control theory and physical rehabilitation to enable mobility in impaired populations.
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