Smart Home Tech: an interface to provide feedback to caregivers of persons living with cognitive impairment
Bibliographic record
Abstract
Abstract Background Home‐based sensor technologies can detect information on daily activities, such as sleep, activity level, and time spent together. This is relevant information for care partners of individuals with cognitive impairment as it can detect early changes in daily activities and cognition. The challenge is how to present thousands of data points to care partners in real‐time, allowing them to make adjustments in their daily routine to help reduce burden. Method The Collaborative Aging Research using Technology (CART) sensor platform was installed in 4 Ottawa homes of persons with cognitive impairment and their care partner. This system includes contact and motion sensors to assess home activity, a smart watch to record steps and sleep patterns, a bed sensor, and medication tracking pillboxes. Questions regarding areas of stress, the types of information that caregivers would like to receive, and how to receive their data were posed in focus group discussions involving caregivers with and without the sensor platform. An interface was designed based on participant input. Result Eight care partners (75% female) aged 30 to 81 participated in focus groups that obtained their opinions on the usefulness of a feedback system, its content, layout, and frequency in which care partners would like to receive their sensor information. The majority of participants wished to be sent their sensor information on a weekly basis in easy to interpret graphical formats that summarized information for them. An algorithm that collects sensor data, parses appropriate measures; steps, sleep, time together in a room, and displays it in graphical representation, was developed. The user interface displays the daily summary compared to the weekly average, with the ability to navigate between day and weekly views. Conclusion Sensor‐based data needs to be displayed in an efficient and convenient manner if it is going to be used by care partners to help guide changes in behavior to reduce stress. This project designed an interface, based on user feedback. This system also has the potential to be used by clinicians to evaluate longitudinal cognitive decline. Next, we will provide participants with the CART system with sample visual reports and gather their feedback.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.037 | 0.006 |
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".