Estimation of Upper-Limb Energy Absorption Capabilities for Stable Human-Robot Interactions
Bibliographic record
Abstract
Human-robot interactions are becoming more and more prevalent in various aspects of life, enhancing humans’ mobility, accessibility and health. However, safety measures need to be addressed when applying robotic-generated forces that put human users at risk. One way to improve safety and performance in robotic tasks is to include physiological information, such as damping properties of the arm, in the control system to help regulate the energy that is delivered to the user. In this work, we estimated the energy absorbing capabilities of the human arm, based on the metric Excess of Passivity (EOP), originally defined in [1]. We used data from healthy subjects to generate models that fit different levels of safety and stability. Variability in subjects’ EOP was a major finding in this study. For demanding applications such as robotic rehabilitation therapy, we suggest using a linear model with two EOP points. Such points are the mean values of EOP estimations at relaxed and rigid levels of hand-grasp forces. Two standard deviations were subtracted from each EOP point to consider the variability due to the neuromuscular changes in the human arm.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".