Classification of Elderly’s Home Activities using Tree-Based Model
Bibliographic record
Abstract
With the aging population, it becomes increasingly essential to prioritize the well-being and safety of older individuals by seeking innovative solutions. One promising approach is the integration of smart home technology equipped with sensors, which can significantly enhance independent living for the elderly. This research centres around the utilization of machine learning techniques to detect and classify activities within smart home environments, specifically focusing on older individuals. By analysing data on their activities and movements, valuable insights can be gained to identify potential behavioural patterns indicating cognitive decline or health issues. The primary goal of the research is to develop robust algorithms capable of accurately identifying and categorizing elderly activities by analysing sensor data collected from various devices. Tree-based approaches, namely Decision Tree and Random Forest, are adopted to achieve their balanced accuracy in categorizing activities and predicting trends and patterns. The results showed that the Random Forest model outperformed the Decision Tree model, achieving a higher balanced accuracy of 69.85% on the training set. This study highlights the immense potential of machine learning in conjunction with smart home technology to significantly improve the lives of the elderly. By accurately identifying and categorizing their activities, caregivers and healthcare professionals can proactively intervene, leading to better outcomes and increased independence for older adults.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".