Challenging Issues of Ambient Activity Recognition for Cognitive Assistance
Bibliographic record
Abstract
In order to provide adequate assistance to cognitively impaired people when they carry out their activities of daily living (ADLs) at home, new technologies based on the emerging concept of Ambient Intelligence (AmI) must be developed. The main application of the AmI concept is the development of Smart Homes, which can provide advanced assistance services to its occupant when he performs his ADLs. The main difficulty inherent to this kind of assistance services is to be able to identify the on-going inhabitant ADL from the observed basic actions and from the sensors events produced by these actions. This chapter will investigate in details the challenging issues that emerge from activity recognition in order to provide cognitive assistance in Smart Homes, by identifying gaps in the capabilities of current approaches. This will allow to raise numerous research issues and challenges that need to be addressed for understanding this research field and enabling ambient recognition systems for cognitive assistance to operate effectively.
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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) | 0.001 | 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".