Utilizing Self-Supervised Learning for Recognizing Human Activity in Older Adults through Labeling Applications in Real-World Smart Homes
Bibliographic record
Abstract
Deep learning models have significantly contributed to recognizing older adults’ daily activities for telemonitoring and assistance. However, recognizing human activities in real-world smart homes over the long term presents substantial challenges. Obtaining the ground truth is time-consuming and costly, yet it is crucial for training and improving deep learning models. Inspired by the impressive performance of self-supervised learning models, this paper utilizes a model based on the SimCLR framework and a self-attention mechanism for downstream human activity recognition. The model leverages the limited and intermittent labeled activities collected by the Label Older Adults’ Daily Activities (LOADA) application, which was deployed and used to acquire activity labels in the real-world, uncontrolled smart homes of three young people and two older adults for over one month. The experimental results demonstrate significant performance in activity recognition, employing semi-supervised learning with limited labels, and transfer learning scenarios where representations learned from one smart home are transferred to another. This research could inspire other human activity recognition community researchers to overcome labeling challenges for monitoring older adults in real-world scenarios.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".