Date of Award

Fall 12-16-2022

Degree Type


Degree Name

Master of Science (MS)


Mathematics and Statistics

First Advisor

Igor Belykh


Stroke therapy is essential to reduce impairments and improve motor movements by engaging autogenous neuroplasticity. This study uses supervised learning methods to address an autonomous classification via stroke severity labeled data by a clinician. Thirty-three patients with chronic stroke performed a variety of rehabilitation activities while utilizing the Motus Nova rehabilitation technology to capture upper and lower body motion. Based on the minimum, maximum, and mean of the range of motion and pressure as well as the number of movements, force flexion, and extension for each game and session provided from the sensor data. Supervised learning methods were applied to a harmonized dataset of roughly 32,000 patient sessions based on the maximum score per session per game. With this approach using light gradient boosting methods we achieved an average of 94% accuracy with 10-fold cross-validation to prevent overfitting. This thesis shows objectively-measured rehabilitation training, enabling the identification of the stroke severity class with the hopes to have patients have a less severe class in the future.

Over the last 10 years robotic rehabilitation has been utilized in inpatient therapy. Robotic rehabilitation has been shown to be effective in improving the severity of stroke in some cases. In particular, robotic devices can be used to help stroke survivors regain movement, improve their functional abilities and improve depression (11). These devices can provide a high level of precision and repeatability, allowing patients to perform ther- apeutic exercises with greater accuracy and consistency (1). Additionally, because robotic devices can be programmed to provide different levels of assistance, they can be tailored to the individual needs of each patient. This allows for a more personalized and effective rehabilitation in-home program (21).


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