Sensor-Based Human Activity Recognition for Elderly In-patients with a Luong Self-Attention Network
Bibliographic record
Abstract
The mobility status of older adults is directly con-nected to their health conditions and physical abilities. Decrease in mobility is a serious issue that leads to adverse health outcomes and declined functional abilities in older adults. Hence, it is highly desirable to assess their mobility status continuously. With the advent of wearable technology, Sensor-based Human Activity Recognition (HAR), which uses measurements from body-worn sensors to gain insights into activities undertaken by individuals, has become an active research field. However, HAR for older adults poses two major challenges: (i) Collecting data from older adults is inherently difficult due to their declining physical abilities, and (ii) Diversity in sensor data distributions is more pronounced across different older adults due to their different mobility statuses. In this paper, we propose an attention-based deep learning model to accurately estimate older adult's mobility through wearable sensor-based HAR. The proposed model achieves a 7.5 % and 7.25 % improvement over baseline methods in a pooling task learning and meta-learning settings on an in-house IMU dataset collected from in-hospital patients, the first of its kind.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".