Modeling behavioral deviations in ADLs using Inverse Reinforcement Learning
Why this work is in the frame
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Bibliographic record
Abstract
The detection of abnormalities in Activities of Daily Living (ADL) has garnered significant attention in recent studies, with many employing deep learning techniques. This paper introduces a novel approach to analyzing ADL sequences, aimed at identifying meaningful deviations from an individual’s routine behavior. Our method offers several benefits for older adults, including timely care, early detection of health conditions to prevent deterioration, reduced monitoring burden on family members, and enhanced self-sufficiency without disrupting daily activities. We propose an Inverse Reinforcement Learning (IRL)-based method to detect behavioral abnormalities in older adults by analyzing ADL sequences. Our approach models the problem of abnormality detection in behavior sequences as a Markov Chain model. By applying the IRL method, we infer the reward function that motivates individuals to perform ADL from observed behavior trajectories. This inferred reward function is then used to identify potential behavior abnormalities through a threshold-based mechanism, where sequences with rewards below a specified threshold are flagged as potential abnormalities.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it