Multimodal System of Ambient Assistance Services for Human Activity Monitoring
Bibliographic record
Abstract
The global demographic has seen a significant surge in the population aged 65 or over in recent years, a trend projected to accelerate in the coming decades.This elderly demographic is progressively losing autonomy, becoming increasingly susceptible to domestic accidents such as falls and heart rhythm abnormalities.To address this, this article introduces a multimodal system designed for continuous monitoring of elderly or disabled individuals within their homes.The developed architecture hinges on a fusion system, integrating signals from acceleration, heart rate, and presence sensors to generate ambient services.These services enable simultaneous detection of heart rate irregularities and falls, as well as tracking the individual's location within their home.Our methodology proposes a conditional fusion, employing IF THEN ELSE rules to produce outputs correlated to the presence or absence of one or more critical situations.This strategy amplifies the accuracy of moving object estimation, particularly during activities of daily living (ADLs), and ensures synchronized assistance services.An emergency service is introduced to classify the urgency and initiate the appropriate action.Validation of the proposed architecture and performance analysis were conducted using the CPNTools tool.Experimental test-based results demonstrated a 78.33% accuracy in the fall detection service.Heart disorder detection service tests confirmed 100% success rate in detecting tachycardia, while the location service demonstrated a sensitivity of 90%.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".