A Quantitative Analysis of Activities of Daily Living: Insights into Improving Functional Independence with Assistive Robotics
Bibliographic record
Abstract
Wheelchair-mounted robotic manipulators have the potential to help the elderly and individuals living with disabilities carry out their activities of daily living (ADLs) independently. Robotics researchers focus on assistive tasks from the perspective of various control schemes and motion types, whereas, health research focuses on clinical assessment and rehabilitation, arguably leaving important differences between the two domains. In particular, there have been many studies on which activities are relevant to functional independence, but little is known quantitatively about the frequencies of ADLs that are typically carried out in everyday life. Understanding what activities are frequently carried out during the day can help guide the development and prioritization of robotic technology for in-home assistive robotic deployment. Robotics and health care communities have differing terms and taxonomies for representing tasks and motions; we aim to ameliorate taxonomic differences by consolidating quantitative task data with prior results from subjective task priority surveys. This study targets lifelogging databases, where we compute (i) daily activity task frequency from long-term low sampling frequency video and Internet of Things sensor data, and (ii) short term arm and hand movement data from video data of domestic tasks. In this work, we aim to provide deeper insights and meaningful guidelines to focus research and future developments in the field of assistive robotic manipulation that support the needs and performance requirements of the target population.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".