A new predictive computer model could help optimize the recovery of stroke patients
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.
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. Fregly and his 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.
Fregly and his team believe this advance is the first step toward the creation of 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.
"Through additional NIH funding, we are embarking with collaborators at Emory University on our first project to predict optimal walking treatments for two individuals post-stroke,” says Fregly. “We are excited to begin exploring whether model-based personalized treatment design can improve functional outcomes."
Muscle Synergies Facilitate Computational Prediction of Subject-Specific Walking Motions. Andrew J. Meyer, Ilan Eskinazi, Jennifer N. Jackson, Anil V. Rao, Carolynn Patten,
Benjamin J. Fregly. Frontiers in Bioengineering and Biotechnology. 2016 Oct 13.
This work was supported by the National Institute of Biomedical Imaging and Bioengineering through grant #R01 EB009351.