Abstract
It is essential to make sure patients be actively involved in motor training using robot-assisted rehabilitation to achieve better rehabilitation outcomes. This paper introduces an attention-controlled wrist rehabilitation method using a low-cost EEG sensor. Active rehabilitation training is realized using a threshold of the attention level measured by the low-cost EEG sensor as a switch for a flexible wrist exoskeleton assisting wrist flexion/extension and radial/ulnar deviation. We present a prototype implementation of this active training method and provide a preliminary evaluation. The feasibility of the attention-based control was proven with the overall actuation success rate of 95%. The experimental results also proved that the visual guidance was helpful for the users to concentrate on the wrist rehabilitation training: two types of visual guidance, namely, looking at the hand motion shown on a video and looking at the user's own hand had no significant performance difference. A general threshold of a certain group of users can be utilized in the wrist robot control rather than a customized threshold to simplify the procedure.
Original language | English |
---|---|
Article number | 8686128 |
Pages (from-to) | 6497-6507 |
Number of pages | 11 |
Journal | IEEE Sensors Journal |
Volume | 9 |
Issue number | 15 |
Early online date | 11 Apr 2019 |
DOIs | |
Publication status | Published - 1 Aug 2019 |
Keywords
- rehabilitation robots
- wrist rehabilitation
- exoskeleton
- brain-controlled robots
- brain-computer interface