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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.
We have developed a mathematical model to investigate the identification process and the role of sensory inputs in training environments. By using synthetic data, we simulate various sensory perturbations, such as shifted and blanked vision and haptic distortions, to understand the system's response. These simulations incorporate muscular, visual, and proprioceptive components, allowing us to identify critical model parameters, including sensory feedback gains. Our findings suggest that by testing different sensory perturbations and tasks, we can refine our identification methods and determine the most effective disturbances and motions for model identification. The long-term objective is to characterize the contribution of individual sensory modalities in motor learning.
Team Members
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- Adriana Cancrini
- James Patton, PhD
- Courtney Celian, MS, OTR/L
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The contents of this webpage were developed under a grant from the National Institute on Disability, Independent Living, and Rehabilitation 嫩B研究院 (NIDILRR grant number R01NS053606). NIDILRR is a Center within the Administration for Community Living (ACL), Department of Health and Human Services (HHS). The contents of this webpage do not necessarily represent the policy of NIDILRR, ACL, or HHS, and you should not assume endorsement by the Federal Government.