Toward Active Physical Human–Robot Interaction: Quantifying the Human State During Interactions
Bibliographic record
Abstract
Unanticipated physical actions from the robot on humans [active physical human–robot interaction (pHRI)] may be inevitable with the deployment of robots in human-populated environments. However, it is still unclear how humans would perceive such actions and how the robot should execute them in a physically and psychologically safe manner. The objective of this article is to explore the possibility of quantifying the humans’ physical and mental state during an active physical interaction with a robot, by means of a laboratory experiment. We hypothesize that the active robot actions could cause measurable alterations in users’ data, which could be related to their perceptions and personalities. In the experiment, the user plays a visual game using the robot, which has a hidden task that results in active physical actions on the user. We collect data from physical and physiological sensors, and the perceptions and personalities via questionnaires and a semi-structured interview. Statistical analysis and clustering of the data collected from a total of 35 participants showed the relationships between participants’ physical and physiological data and their age, gender, perception, and personalities. Further developments based on these exploratory outcomes can be used to implement an active pHRI controller that can account for both the physical and the mental state of users.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".