Force Exertion Affects Grasp Classification Using Force Myography
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Bibliographic record
Abstract
This paper describes a study that explores the force exertion effect on the classification of grasps using a force myography (FMG) technology. Nine participants were recruited to the study; each performed a set of 16 different grasps from a grasp taxonomy using eight different levels of force, respectively. Their wrist muscle pressure was recorded using an array of 16 force sensing resistors. A linear discriminant analysis model was trained by grasps at a single force level using the natural grasping force to classify grasps generated by eight different levels of force. The results show that the grasping force significantly affects the accuracy of grasp classification such that a grasping force closer to the natural force achieves a higher accuracy. A still acceptable classification performance can be achieved for approximately half of the natural grasping force. The findings of this study help the understanding of how force exertion can affect grasp recognition using FMG. Knowledge gained from this study will provide guidance for the implementation of gesture control interfaces in terms of grasping force variations.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it