Podium 1: Skeletal Disorders, Treatments & Rehabilitation

Kayla Pariser, Jill Higginson

University of Delaware

Treadmill gait training is frequently prescribed for individuals poststroke to promote improved walking function. However, with commonly used fixed-speed treadmill paradigms only 50% of stroke survivors improve gait mechanics, possibly because fixed-speed treadmills limit natural stride-to-stride variability essential for motor learning. To address this concern, we developed an adaptive treadmill (ATM) that adjusts belt speed in real-time via changes in user propulsion and step length, promoting healthy dynamic variability. While healthy young adults improve gait mechanics with the ATM versus fixed-speed treadmills, the response of stroke survivors to the ATM is mixed, perhaps due to lack of controller customization. Computational treatment design using predictive simulations may allow for more efficient selection of optimal rehabilitation compared to fatiguing trial-and-error experiments. The purpose of this study was to develop and evaluate a predictive simulation framework to estimate changes in gait with various ATM controllers. With musculoskeletal modeling and optimal control methods we simulated different ATM controllers we previously tested experimentally. The ATM simulation framework successfully captured changes in gait mechanics that we observed experimentally. This is the first study to show how computational modeling can inform design of ATM controllers and test hypotheses regarding how individuals will respond to novel ATM controllers.

Research Area: Neuromuscular Modeling & Control