Development of a computerized visual feedback system to re-educate functional pinch in patients with motor or sensory deficits
Bibliographic record
Abstract
Precision grip, such as lateral pinching, is an essential component of independent hand function. This apparently simple motor task, relies on a complex interaction of sensory and motor mechanisms. Patients with impaired input from proprioceptors and cutaneous receptors may experience difficulty with timing and scaling motor recruitment during manipulative manoeuvres. To help evaluate and retrain motor control of the hand, an instrumented pinching device with a computerized on-line visual feedback system responding to pinch force variation was designed and constructed. Mechanical testing of the device included step loading, loading-unloading cycles and 4 hours of constant loading to evaluate hysteresis, nonlinearity and signal shift over time. Calibration coefficients were calculated in the active range of 0 to 100 Newtons. A pilot test was then conducted using normal subjects (n=6). The experimental session for each subject consisted of a total of 30 trials: one trial per pinch span (12.3, 31.4 and 51.7 mm) to determine the maximal force (MF), and three repeated measures per force target (FT) level (25, 50 and 75 per cent of MF) per span. Data was filtered (Fc=10 Hz) and then normalized in terms of Ff level. Overall, subjects achieved better pinch force control at lower FT levels and at the thickest span tested. This study provided the basis for a clinical pilot test to determine the effectiveness of this rehabilitation tool in re-educating functional pinch in patients with sensorimotor impairments.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".