Bibliographic record
Abstract
Older adults sometimes forget about whether or not they have completed routine actions and the states of objects that they have interacted with (e.g., the kitchen stove is on or off). In this work, we explore whether video clips captured from a body-worn camera every time objects of interest are found within its field of view can help older adults determine if they have completed certain actions with these objects and what their states are. We designed FMT ("Fiducial Marker Tracker")---a real-time capture and access application that opportunistically captures video clips of objects the user interacts with. To do this, the user places fiducial markers close to objects which would be captured when the marker enters the user's body-worn camera's field of view. We examine and discuss what objects this system would be best suited to track, and the usefulness and usability of this approach. FMT successfully captured direct interactions with an object at an average rate of 75.6% across all participants (SD = 9.9%). Our results also reveal how, what, and why users would use such a system for help.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.304 | 0.165 |
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".