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Assist-As-Needed Exoskeleton for Hand Joint Rehabilitation Based on Muscle Effort Detection
| Content Provider | MDPI |
|---|---|
| Author | Castiblanco, Jenny Mondragon, Ivan Alvarado-Rojas, Catalina Colorado, Julian |
| Copyright Year | 2021 |
| Description | Robotic-assisted systems have gained significant traction in post-stroke therapies to support rehabilitation, since these systems can provide high-intensity and high-frequency treatment while allowing accurate motion-control over the patient’s progress. In this paper, we tackle how to provide active support through a robotic-assisted exoskeleton by developing a novel closed-loop architecture that continually measures electromyographic signals (EMG), in order to adjust the assistance given by the exoskeleton. We used EMG signals acquired from four patients with post-stroke hand impairments for training machine learning models used to characterize muscle effort by classifying three muscular condition levels based on contraction strength, co-activation, and muscular activation measurements. The proposed closed-loop system takes into account the EMG muscle effort to modulate the exoskeleton velocity during the rehabilitation therapy. Experimental results indicate the maximum variation on velocity was 0.7 mm/s, while the proposed control system effectively modulated the movements of the exoskeleton based on the EMG readings, keeping a reference tracking error <5%. |
| Starting Page | 4372 |
| e-ISSN | 14248220 |
| DOI | 10.3390/s21134372 |
| Journal | Sensors |
| Issue Number | 13 |
| Volume Number | 21 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-06-26 |
| Access Restriction | Open |
| Subject Keyword | Sensors Robotics Active Control Robotic-assisted Systems Emg Control Stroke Rehabilitation Hand Motion Rehabilitation Hand Exoskeleton Orthosis Assist-as-needed System Feedback-fuzzy |
| Content Type | Text |
| Resource Type | Article |
