Remote Control of Hand Actuators via Glove Sensors for Medical Care Applications
Bibliographic record
Abstract
Early diagnosis of psychomotor diseases such as Parkinson's requires timely and effective medical care, which is often expensive and resource‐intensive. This study proposes a remote‐control system for assisting medical care related to hand movement. Human hand motion is captured using a comfortable, wearable sensory glove, while actuation is achieved via a fabric‐based pneumatic system that drives finger bending. Finite element modeling is conducted to examine how the ratio of the stiff to soft sheet's Young's modulus affects actuator performance, showing that increased ratios lead to greater bending angles. A machine learning model is developed to relate finger angle to actuator pressure. For remote operation, data from the glove are transmitted—physically or virtually—to a separate system, where a medical professional controls the actuator using MATLAB‐based algorithms. This teleoperation method for healthcare is relatively unexplored in current literature. In addition to medical applications such as rehabilitation or Parkinson's monitoring, the system offers the potential for reducing human risk in hazardous settings—such as operating heavy industrial machinery, handling high‐risk lab chemicals, or performing maintenance in contaminated environments.
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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 | 0.000 | 0.001 |
| 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) | 0.000 | 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".