Usability testing of a mobile robotic system for in-home telerehabilitation
Bibliographic record
Abstract
Mobile robots designed to enhance telepresence in the support of telehealth services are being considered for numerous applications. TELEROBOT is a teleoperated mobile robotic platform equipped with videoconferencingcapabilities and designed to be used in a home environment to. In this study, learnability of the system's teleoperation interface and controls was evaluated with ten rehabilitation professionals during four training sessions in a laboratory environment and in an unknown home environment while performing the execution of a standardized evaluation protocol typically used in home care. Results show that the novice teleoperators' performances on two of the four metrics used (number of command and total time) improved significantly across training sessions (ANOVAS, p<0.05) and that performance in these metrics in the last training session reflected teleoperation abilities seen in the unknown home environment during navigation tasks (r=0,77 and 0,60). With only 4 hours of training, rehabilitation professionals were able learn to teleoperate successfully TELEROBOT. However teleoperation performances remained significantly less efficient then those of an expert. Under the home task condition (navigating the home environment from one point to the other as fast as possible) this translated to completion time between 350 seconds (best performance) and 850 seconds (worse performance). Improvements in other usability aspects of the system will be needed to meet the requirements of in-home telerehabilitation.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.001 | 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".