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) in Bethesda, MD have developed a computational walking model that could help guide patients to their best possible recovery after a stroke.
The 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.
The approach was tested on a patient who had suffered a stroke. The team first measured how the patient walked at his preferred speed on a treadmill. Using those measurements, they then constructed a neuromusculoskeletal computer model of the patient that was personalized to the patient's skeletal anatomy, foot contact pattern, muscle force-generating ability, and neural control limitations. The team found that the personalized model was able to predict accurately the patient's gait at a faster walking speed, even though no measurements at that speed were used for constructing the model.
The researchers believe this advance is the first step toward designing personalized neurorehabilitation prescriptions, filling a critical gap in the current treatment planning process for stroke patients. Together with devices that would ensure the patient is exercising using the proper force and torque, personalized computational models could one day help maximize the recovery of patients who have suffered a stroke.
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