S3‐05–06: An intelligent enviornment to support aging‐in‐place and independence
Bibliographic record
Abstract
An older adult with Alzheimer's disease (AD) is often unable to independently complete activities of daily living (ADLs) because he/she cannot remember the proper sequence of steps that must be completed. The long-term goal of this research program is to develop an intelligent home that is able to monitor an older adult with AD, providing reminders, prompts, and guidance as necessary. This assistance may include relatively simple reminders to help find misplaced items, reminders and monitoring of critical events (e.g., medication), and/or actively monitoring the person through the various ADL tasks/steps. We have been working on various intelligent home systems to assist older adults with AD to complete some of the above described activities/tasks. Specifically, we have been developing two systems: 1) the COACH, an intelligent prompting system to help guide older adults with dementia through common self-care activities; and 2) HELPER, an intelligent emergency response and fall detection system. We have applied advanced artificial intelligence and sensing techniques based on computer vision that allow these systems to monitor a user as he/she completes a specific ADL, such as handwashing, as well as tracking overall body movements and postures. These techniques also allow the systems to learn about, and automatically adapt to each user. Efficacy studies for each of these systems have been conducted in both simulated and clinical environments. The COACH was recently tested with six older adults with moderate-to-severe dementia in a long-term care facility. It was observed that when the prompting system was used there was on average a 25% increase in the number of handwashing steps they were able to complete without the need for interactions with a caregiver. The HELPER system, specifically the fall detection module, was tested in a simulated home environment. These trials found that the system was able to correctly classify approximately 77% of all falls that occurred. This symposium will present these two systems, including technical achievements, examples of them in use, and the efficacy studies and resulting data.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".