Towards a brain computer interface driven exoskeleton for upper extremity rehabilitation
Bibliographic record
Abstract
Stroke impairs individuals to perform activities of daily living. Intense rehabilitation programs offer hope for recovery, but are labor intensive and costly. Robotic rehabilitation technology plays a key role to solve such a problem. Current robotic systems along with brain computer interface (BCI) allow patients to participate in rehabilitation exercises, which require their own mental inputs. Studies have shown such active rehabilitation exercise can induce neuroplasticity and help towards recovery. However, even though BCI-driven robotic systems do exist, they are large complex systems and expensive to set up. These drawbacks limit a wide distribution of these technologies. Currently, the BCI robotic systems only used in large hospitals or research settings, not community level facilities. To facilitate the accessibility of stroke patients to such technologies, we propose a novel BCI-driven exoskeleton rehabilitation system. The exoskeleton has four degrees of freedom (DOF) for assisting the movement of the upper extremities. It is integrated with an affordable and wireless EEG headset for enabling the patients to control the movement of the exoskeleton with their brain activity. The developed exoskeleton is portable and easy to set up. A sequential control scheme is proposed to allow the user to control one movement at a time. An experiment was designed to assess if a healthy individual was able to control the movement of the exoskeleton correctly under a predefined sequence. One volunteer participated in the exploratory study and the volunteer was able to correctly control the exoskeleton in each step.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".