Regressing force-myographic signals collected by an armband to estimate torque exerted by the wrist: A preliminary investigation
Bibliographic record
Abstract
Human-machine-interfaces (HMI) have a key role for translating human intention into control commands to external devices. Different wearable techniques, including surface electromyography (sEMG), have been proposed for acquiring bio-signals that reveal the human intention. In this paper, we explore an easy-to-use wearable sensor device that can be used to measure force-myography (FMG) signals. We assess if FMG signals can be used to estimate isometric torque of hand pronation-supination, wrist flexion-extension or wrist radial-ulnar using a regression model. Results of our investigation report an average accuracy over 90%. The related standard deviation of 0.02 is showing consistency of data among different data collecting sessions. The proposed FMG-based device shows therefore promising performance for different future applications, which may include: monitoring the progress of patients during exercising in arm rehabilitation programs; proportional control of robotic hand prosthesis; and control of robot movements.
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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 | 0.003 |
| 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) | 0.001 | 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 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".