Assistive arm and hand manipulation: How does current research intersect with actual healthcare needs?
Bibliographic record
Abstract
Human assistive robotics have the potential to help the elderly and individuals living with disabilities with their Activities of Daily Living (ADL). Robotics researchers present bottom up solutions using various control methods for different types of movements. Health research on the other hand focuses on clinical assessment and rehabilitation leaving arguably important differences between the two domains. In particular, little is known quantitatively on what ADLs humans perform in their everyday environment - at home, work etc. This information can help guide development and prioritization of robotic technology for in-home assistive robotic deployment. This study targets several lifelogging databases, where we compute (i) ADL task frequency from long-term low sampling frequency video and Internet of Things (IoT) sensor data, and (ii) short term arm and hand movement data from 30 fps video data of domestic tasks. Robotics and health care communities have different terms and taxonomies for representing tasks and motions. We derive and discuss a robotics-relevant taxonomy from this quantitative ADL task and ICF motion data in attempt to ameliorate these taxonomic differences. Our statistics quantify that humans reach, open drawers, doors, and retrieve and use objects hundreds of times a day. Commercial wheelchair mounted robot arms can help 150,000 upper body disabled in the USA alone, but only a few hundred robots are deployed. Better user interfaces, and more capable robots can increase the potential user base and number of ADL tasks solved significantly.
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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 | 0.015 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.010 | 0.020 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".