On the estimation of isometric wrist/forearm torque about three axes using Force Myography
Bibliographic record
Abstract
Much of the morbidity and disability associated with industrial work settings arise from accidents involving humans and robots. Force Myography (FMG) is a potential technique to be used as an additional control measure for safer human-robot interaction without the need for robot hardware modification or replacement. The FMG signals represent the volumetric changes in the forearm due to muscle contraction, which were acquired using a Force Sensitive Resistor strap. A 1DOF torque sensor was used to model the point of interaction between a robot and a human. The following isolated upper extremity movements were considered: forearm pronation-supination, wrist flexion-extension and wrist radial-ulnar deviation. Torque regression models based on FMG data were created with two machine learning methods: Support Vector Machine (SVM) and Artificial Neural Network (ANN). Performance indices were defined and used for the comparative study between the two learning methods. The results demonstrated the feasibility of using FMG to estimate torque with accuracies around 90%. Both methods also demonstrated strong intra- and inter- participant consistency of FMG signals. The results will be beneficial for measuring the contact force between human and robot during their interaction.
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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 | 0.001 | 0.001 |
| 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".