Implementing an intelligent video monitoring system to detect falls of older adults at home: a multiple case study
Bibliographic record
Abstract
Purpose Older adults are at a high risk of falling. The consequences of falls are worse when the person is unable to get up afterward. Thus, an intelligent video monitoring system (IVS) was developed to detect falls and send alerts to a respondent. This study aims to explore the implementation of the IVS at home. Design/methodology/approach A multiple case study was conducted with four dyads: older adults and informal caregivers. The IVS was implemented for two months at home. Perceptions of the IVS and technical variables were documented. Interviews were thematically analyzed, and technical data were descriptively analyzed. Findings The rate of false alarms was 0.35 per day. Participants had positive opinions of the IVS and mentioned its ease of use. They also made suggestions for improvement. Originality/value This study showed the feasibility of a two-month implementation of this IVS. Its development should be continued and tested with a larger experimental group.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".