Benefits of automatic human action recognition in an assistive system for people with dementia
Bibliographic record
Abstract
In the context of Activities of Daily Living, a human action can be defined as any interaction between an individual and his or her environment during a task (e.g., to use the soap during handwashing). When an assistive system is designed to recall users what to do during a task, one of its goals is to properly track and detect their actions in order to provide accurate guidance. This paper describes a k-Nearest Neighbor (kNN) based Action Recognition System (ARS) for use in COACH, which is an assistive technology designed for people with dementia. The kNN-based ARS is able to recognize 6 main actions related to the handwashing task. The recognition is done in real-time and uses continuous sequences of discrete hand positions output by a handtracker. The aims of this new ARS are (1) to verify the benefits of enabling automatic human action recognition in COACH, (2) to evaluate its ability to overcome the limitations experienced during previous clinical trials.
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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 | 0.002 |
| 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".