After a stroke, patients typically have trouble walking and few are able to regain the gait they had before suffering a stroke. Researchers funded by the National Institute of Biomedical Imaging and Bioengineering (NIBIB) have developed a computational walking model that could help guide patients to their best possible recovery after a stroke. Computational modeling uses computers to simulate and study the behavior of complex systems using mathematics, physics, and computer science. In this case, researchers are developing a computational modeling program that can construct a model of the patient from the patient’s walking data collected on a treadmill and then predict how the patient will walk after different planned rehabilitation treatments. They hope that one day the model will be able to predict the best gait a patient can achieve after completing rehabilitation, as well as recommend the best rehabilitation approach to help the patient achieve an optimal recovery.
Currently, there is no way for a clinician to determine the most effective rehabilitation treatment prescription for a patient. Clinicians cannot always know which treatment approach to use, or how the approach should be implemented to maximize walking recovery. B.J. Fregly, Ph.D. and his team (Andrew Meyer, Ph.D., Carolynn Patten, PT., Ph.D., and Anil Rao, Ph.D.) at the University of Florida developed a computational modeling approach to help answer these questions. They tested the approach on a patient who had suffered a stroke.
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