A Comparative Study of Sensor Quantity and Data Granularity in AAL Systems for Activity Recognition
Bibliographic record
Abstract
Abstract As populations age, the demand for non-intrusive elderly care solutions increases, highlighting the need for efficient AAL systems. Current research predominantly focuses on wearable sensors at high data frequencies. There is a critical gap in research in understanding the effectiveness of model performance using ambient sensors operating at varied granularities and with varied numbers of sensors. This study addresses this gap by investigating how sensor quantity and data frequency affect an artificial intelligence model’s ability to detect various activities of daily living. The methodology employs a quantitative, experimental design to systematically assess the performance of artificial intelligence models across sensor quantity and data frequencies. This assessment will be conducted through a multi-study approach involving different populations to ensure robust and generalizable findings. Each model’s efficacy will be evaluated using 5-fold cross-validation and GridSearchCV for rigorous hyperparameter tuning, employing diverse data aggregation and imputations techniques to maintain comprehensive analysis integrity. The primary goal of this research is to determine the optimal data granularity and number of sensors that maximize AI models’ ability to detect daily living activities while minimizing resource demands, thereby enhancing the sustainability and scalability of AAL systems. This work aims to advance the field of ambient sensing in elderly care, offering significant implications for designing and implementing future AAL technologies and potentially improving the quality of life for the elderly population. Key messages • The study explores the optimal configuration of data granularity and sensor quantity to maximize AI model efficiency in detecting activities of daily living among older adults. • The study investigates the impact of data frequency and sensor count on AI model performance in detecting daily activities, aiming to optimize Ambient Assisted Living systems for the elderly.
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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 | 0.007 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".