Hand assistive device with suction cup (HADS) technology for poststroke patients
Bibliographic record
Abstract
A stroke is a neurological disease that primarily causes paralysis. Besides paraplegia, all other types of paralysis affect the upper extremity. Advanced technologies, such as wearable devices and rehabilitation regimens, are also being developed to enhance the functional ability of a stroke person to grasp and release daily living objects. In this research, we developed a rehabilitation functional assist device combining a flexion and extension mechanism with suction cup technology (hybrid technology) to help post-stroke patients improve their hand grip strength in day-to-day grasping activities. Ten poststroke hemiplegia patients were studied to test the functional ability of the impaired hand by wearing and not wearing the device. The outcomes were validated by three standard clinical tests, such as the Toronto Rehabilitation Institute – Hand Functional Test (TRI-HFT), the Chedoke Arm Hand Activity Inventory (CAHAI-9), and the Fugl-Meyer Assessment (FMA) with overall score improvements of 14.5 ± 3.8–25 ± 2.2 ( p = 0.005), 5.4 ± 2.8–10 ± 1.6 ( p = 0.008), and 9.6 ± 2.6–17 ± 2.4 ( p = 0.005) respectively. The p-value for each of the three evaluations was less than 0.05, indicating significantly improved results and the average feedback score of the participants was 3.8 out of 5. The proposed device significantly increased impaired hand functionality in post-stroke patients. The subjects could complete some of the grasping tasks that they could not grasp without the device. Clinical trial registration The Clinical Trial Registry of India approved the work CTRI/2022/02/040495 described in this manuscript.
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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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".