A common rehabilitative technique for those with neuro-muscular disorders is functional electrical stimulation (FES) induced exercise such as FES-induced biceps curls. FES has been shown to have numerous health benefits, such as increased muscle mass and retraining of the nervous system. Closed-loop control of a motorized FES system presents numerous challenges since the system has nonlinear and uncertain dynamics and switching is required between motor and FES control, which is further complicated by the muscle having an uncertain control effectiveness. An additional complication of FES systems is that high gain feedback from traditional robust controllers can be uncomfortable to the participant. In this paper, data-based, opportunistic learning is achieved by implementing an integral concurrent learning (ICL) controller during a motorized and FES-induced biceps curl exercise. The ICL controller uses adaptive feedforward terms to augment the FES controller to reduce the required control input. A Lyapunov-based analysis is performed to ensure exponential trajectory tracking and opportunistic, exponential learning of the uncertain human and machine parameters. In addition to improved tracking performance and robustness, the potential of learning the specific dynamics of a person during a rehabilitative exercise could be clinically significant. Preliminary simulation results are provided and demonstrate an average position error of 0.14 ± 1.17 deg and an average velocity error of 0.004 ± 1.18 deg/s.

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Tremor is the most common movement deficit and manifests in a variety of disorders, including Essential Tremor, Parkinson's Disease, Dystonia, and Cerebellar Ataxia. Although medication and surgical interventions have significantly reduced patient suffering, they are only partially effective and can carry undesired side effects, leaving many patients without satisfactory treatment options. Wearable tremor-suppressing devices could provide an alternative to medication and surgery. Multiple research groups have developed orthotic prototypes to low-pass filter tremor, but these devices have not yet been optimized for in-vivo use. Optimizing non-invasive tremor suppression requires an understanding of where the tremor originates mechanically (which muscles) and how it propagates to the hand (where it matters most). Here we present on the beginnings of our multi-pronged work to determine the origin, propagation, and distribution of Essential Tremor, and we provide preliminary results.

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