On the relationship between autonomy, performance, and satisfaction: Lessons from a three-week user study with post-SCI patients using a smart 6DOF assistive robotic manipulator
Bibliographic record
Abstract
The UCF-MANUS, a vision-based 6DOF assistive robotic arm, has been designed to aid individuals with arm function limitations to complete tasks of daily living that they would otherwise be unable to complete themselves. This paper reports a small dual cohort pilot study with traumatic spinal cord injured (SCI) subjects designed to investigate the utility of the UCF-MANUS for these subjects. Pick-and-place ADL tasks were de÷ned and users trained and tested with the system for three weeks during which they controlled the robot either through a manual or an autonomous (supervised) mode of operation. Baseline characteristics (pre-study), quantitative performance metrics (during study) and psychometrics (post-study) were obtained and statistically analyzed to test a set of hypotheses related to performance and satisfaction with the two control modes. It was seen that manual interaction showed more variability and inef÷ciency in performance metrics as compared to autonomous operation. Suprisingly the latter mode, however, did not lead to better measures for user satisfaction. A discussion is provided to explain the results. Based on qualitative feedback and quantitative results, possible directions for system design are presented in order to concurrently achieve better performance and satisfaction outcomes.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".