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This multi-site collaborative research study will explore the effectiveness of different strategies aimed at improving or maintaining your physical activity.

The Center for Aphasia Research and Treatment offers a variety of groups and classes for people with aphasia who are living in the community. All classes help the participant practice communicating in a supportive group environment.

This system is a very straightforward use of the looking glass display system combined with a tracking device called the Leap? tracking device. It tells the computer where your hands are, and then we ask the patient to do a bimanual task to move a virtual tray to different locations in space without letting a ball roll off.

Can you build a minimally-actuated exo-robot as a wearable orthosis? Maybe one that is most simply built out of springs? It may not do everything, but what can you do? The secret is to allow networks of springs, and structural optimization algorithm that tells us how to build it.

Is it possible to use a network of springs to make up for what the muscles do during walking and hence assist gait? This study shows the potential of the ExoNET device to reproduce the torques generated by your muscles while you walk. We feel that this structural design can guide devices in the future and may lead to clinical tools that are lightweight, unintimidating, easy to use, and inexpensive.

Can you build a soft, exo-robot as a wearable orthosis to provide assistance during both rehabilitation and activities of daily living? Can this same device also be used as a therapeutic device by tuning to anti-assistance mode, providing more meaningful therapy to the user?

Body Computer Interface (BCI) is the idea that one can control a robot simply by thinking about it. In this study, we are laying the groundwork for further BCI and robotic development for individuals to control a hand opening device called the Electro encephalographic mediated glove (or Eglove) using an EEG cap connected to a motorized glove.

Sensory inputs such as vision, proprioception, and touch play a crucial role in post-stroke recovery. Our research delves into how these sensory contributions can be assessed to develop effective, personalized therapy strategies. Enhancing and tailoring sensory inputs to an individual¡¯s needs allows us to explore how learning outcomes can be improved and errors reduced. Through synthetic simulations that combine muscular, visual, and proprioceptive inputs, we aim to understand better the complex processes involved in motor learning.

True behavioral restitution, a return to normal motor patterns with the affected limb post-stroke, requires the recruitment and restoration of the residual ipsilesional hemisphere/corticospinal tract (CST). Following stroke, the spontaneous recovery mechanism selectively and continuously uses a more optimized neural network for motor execution, depending on the degree of CST damage.

Stroke survivors vary greatly in their motor deficits and rehabilitative needs. Here, we gather unstructured upper limb movement data and seek to understand if there are patterns in their kinetics that reflect the underlying neuromuscular alterations. In doing so, we can improve our abilities to evaluate patients and design personalized rehab therapy.

This study utilizes a wearable data glove system that translates hand movements into signals that control a cursor on a screen. We examined how participants learn a redundant novel task, which can be completed through various solutions.

During TMSKR nerves in an amputee¡¯s residual arm are transferred to target muscles that no longer perform a useful function because of the amputation.