An estimated 2.2 million Americans are suffering from limb loss today [ZieglerEA2008APMR]. With conventional passive prostheses, people with lower-limb loss often struggle to perform the various activities of daily life since these devices cannot recreate the full energetically active function of the missing limb. Powered prosthetic legs offer a partial solution, but state-of-the-art control for prosthetic legs almost universally switches between states of a finite state machine, a strategy that both requires elaborate user-specific tuning and is inherently task-dependent [TuckerEA2015JNER]. An emerging alternative control approach, which I co-developed with Dr. Gregg in the context of exoskeletons, uses dynamical system state-estimation to overcome this task-dependency and allow an estimator to continuously track shifting locomotion tasks in real-time. The method extends Dr. Gregg’s prior approach [RezazadehEA2019IA] by fusing all possible sensors rather than the thigh angle alone and by estimating continuous task variables as the stride is in progress rather than after the stride is over. This dynamical system includes a single “gait-phase” oscillator as well as a bank of gait parameters ranging from concrete measures like the ground inclination to abstract descriptions of what differentiates one stride from another. These descriptions could encode, for example, a “gait-compensation-fingerprint” that describes how an individual stride has been volitionally altered from the baseline pattern.
Such compensations could then be mapped to degrees of freedom in the powered prosthesis that the patient would otherwise be unable to control, such as swing height or propulsion during stance. More specifically, my aims are to (1) model a practical subset of the possible modes of human locomotion as the oscillation of a single, fundamentally low-dimensional dynamic system using a bio-mechanical dataset of able-bodied human locomotion, (2) apply nonlinear state-estimation to estimate the low-dimensional dynamic system’s state in real-time based on all the sensor information available to the prosthesis,
and (3) determine the extent to which participants can control the device when this gait-state estimate is mapped to the “corresponding” robot behavior. As this research addresses a key technological barrier to biomedical adoption of powered prosthetic devices, it would align with the missions of both the NIH NIBIB and the NSF CBET program on Disability and Rehabilitation Engineering.
Human-Empowering Robotics and Control (HERC) Lab
Empowering Humans with Robots
