Adaptation of task difficulty in rehabilitation exercises based on the user´s motor performance and physiological responses

Although robot-assisted rehabilitation regimens are as effective, functionally, as conventional therapies, they still lack features to increase patients´ engagement in the regimen. Providing rehabilitation tasks at a “desirable difficulty” is one of the ways to address this issue and increase the motivation of a patient to continue with the therapy program. Then the problem is to design a system that is capable of estimating the user´s desirable difficulty, and ultimately, modifying the task based on this prediction. In this paper we compared the performance of three machine learning algorithms in predicting a user´s desirable difficulty during a typical reaching motion rehabilitation task. Different levels of error amplification were used as different levels of task difficulty. We explored the usefulness of using participants´ motor performance and physiological signals during the reaching task in prediction of their desirable difficulties. Results showed that a Neural Network approach gives higher prediction accuracy in comparison with models based on k-Nearest Neighbor and Discriminant Analysis methods.