Embedded System for Interactive Pneumatic Hand Rehabilitation: Real-Time Gaming Interface With Cognitive Stimulation for Motor Recovery
Bibliographic record
Abstract
Restoring fine motor skills in individuals with upper extremity sensory-motor post-stroke impairments necessitates repetitive, task-specific exercises to promote functional recovery. Given the substantial time commitment required for therapy, rehabilitation tools must be not only effective, but also engaging by adopting a game-based approach to mitigate the monotony of prolonged repetitive exercises. This paper presents a user-friendly finger-thumb mechanism designed to support the index and middle fingers as well as the thumb, specifically for patients with hand injuries. The device establishes a wireless connection to a gaming platform enabling patients to engage with computer games in real-time through purposeful thumb and finger movements. This connectivity is established through two Raspberry Pi boards utilizing a server-client network. Additionally, the device incorporates assistive or resistive forces during gameplay to adjust the level of assistance or challenge based on the individual’s motor control proficiency. With a minimal data transfer delay of 10 ms, and a 50 ms delay for game event updates, patients can seamlessly participate in the gaming experience and modify events in real-time through the newly developed wearable device. In the experiments, assistive mode achieved a 100Movement errors varied across modes, with assistive mode showing the lowest errors, indicating more accurate and consistent performance.
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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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".