A New Hand-Measurement Method to Simplify Calibration in CyberGlove-Based Virtual Rehabilitation
Bibliographic record
Abstract
We have previously developed a prototype virtual-reality-enhanced rehabilitation system using the CyberForce system to assist patients who have suffered from upper extremity stroke to practice some daily life exercises. However, full calibration of the system for each patient is currently not only tedious and time consuming but also impractical in the case of severely disabled hands. In this paper, we propose a practical and easy-to-perform hand-measurement method to calibrate the CyberGlove using artificial neural networks (NNs). The NNs are trained with the hand-segment sizes as input and the manually collected calibrated data as output. The only external device needed is a 2-D digital camera to take the picture of the subject's hand against a chessboard for the hand-segment-size measurement. Subjective evaluation results for various common hand postures show the effectiveness of the proposed method.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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 teacher head, 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".