Modeling contact forces during human-robot interactions for performing activities of daily living
Bibliographic record
Abstract
This study aims to characterize contact forces between humans and tools during activities of daily living (ADL) to provide information to a personal care robot. The study was conducted on non-impaired subjects to capture various static and dynamic force levels when making contact with three different robotic tools, each designed to perform an ADL task: hair brushing, face wiping, and face shaving. The static trial of the study involved 21 participants. Forces were collected at fixed locations for each task and used to develop models for each participant. Extraction of the maximum force levels was performed for both the maximum and desired levels of force. The dynamic trial involved 24 individuals. Participants were asked to maintain a comfortable level of force for the duration of their contact with the tool as the robot moved along its path to perform the ADL task. For the static and dynamic trials, higher forces were observed during hair brushing compared to the other two tasks. It was observed that the hair brushing task force at a specific contact point has an overall maximum of 55.66 N, while the maximum forces detected in the face wiping and face shaving tasks were 36.40 and 11.11 N, respectively. The forces collected were analyzed, and no trends were found relating the contact forces to the gender, height, or weight of the subjects. Based on the analysis of the results, recommendations have been made to enhance the force safety limits for the personal care robot working environment.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".