Quantifying Human Mental State in Interactive pHRI: Maintaining Balancing
Bibliographic record
Abstract
As robots increasingly enter domestic environments, investigating the impact of their physical behaviors and the potential to leverage human mental states during interaction becomes crucial. This study examines how a robot's active behavior (unanticipated physical actions) versus passive behavior (actions aligned with user expectation) affects users' mental states during a physical balance task. Our findings show that passive interaction is generally more cognitively ergonomic, while active behavior, though it reduces imbalance, adds cognitive strain. Users' perceptions of the robot are not affected by its behavior type. We conclude that combining peripheral skin temperature with age and personality traits holds significant potential for enhancing robots' ability to infer users' cognitive ergonomics and belief levels. This study explores the relatively under-researched area of active behavior in physical assistive applications with minimal sensor requirements and identifies easily obtainable online data as indicators of human mental state.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".