Pilot study on fine finger movement regression, using FMG
Bibliographic record
Abstract
Predicting hand gestures and finger movements include a wide range of applications in different fields such as human computer interaction, rehabilitation, and prosthesis control. Research in this area, mainly focuses on hand gesture classification which limits the ability of the system to a set of predefined gestures and also limits the ability to control fine finger movements. Force Myography (FMG) is a novel method in which the volumetric change of the muscles associated with a functional motor movement is measured. In this study, the feasibility of using the FMG signals for predicting fine finger movements, and the effect of the hand movement on the prediction was investigated. To obtain the FMG signals, an array of 16 Force Sensing Resistors (FSR) was utilized. To record the trajectory of finger movements, eight calibrated infrared cameras were used. Ten reflective markers, were placed on the index and middle fingers, the thumb and the back of the hand. The FMG signals and the location of the markers were collected while the participant placed their hand in three different predefined locations parallel to the sagittal plane passing through humerus and performed three different hand gestures. FMG signals were collected, and the marker trajectories were fed into a Random Forest Regression algorithm. The results showed an average squared correlation coefficient higher than 75%, on different hand gestures and locations, which proves the feasibility of using FMG signals to predict fine finger movements, in three predefined locations, for three different hand gesture.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".