Blockchain-based Model for Sharing Activities of Daily Living in Healthcare Applications
Bibliographic record
Abstract
People aged 65 and above are considered to be the fastest-growing population in the world. It is believed that the majority of elderly people will be living alone and hence need to receive health services to ensure their well-being. One promising way is to provide caregivers access to the elderly daily activities (e.g., eating, cleaning, exercising patterns, etc.) through non-intrusive means. Therefore, recognizing and tracking their daily activities play a key role in providing timely health and emergency services that foster better day-to-day care. The solution proposed in this paper is based on analyzing and recognizing the daily activities based on the energy consumption of appliances inside homes. To ensure that elderly people's data are protected and accessible by authorized personnel within the healthcare ecosystem, blockchain technology is used as a means for maintaining and sharing daily activities patterns with healthcare providers. The main challenges of this paper are as follows: (i) How to recognize relationships between appliance usage and daily activities from concurrent streams of smart meter data; and (ii) how to construct profiles of daily activities and upload them to the blockchain system. For addressing these issues, we develop our model based on Bayesian network to recognize daily activities based on the energy consumption of appliances. To ensure that elderly people's data are protected, we use Hyperledger blockchain technology as a trustworthy mechanism for creating profiles, in order to protect data and prevent scams or frauds that might jeopardize people's privacy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".