Web-based human activity recognition using images and descriptive web pages
Bibliographic record
Abstract
Loss of autonomy is a problem that researchers have been trying to solve for the last two decades thanks to technologies that facilitate the prolongation of home care. In order to ensure the well being and security of residents in loss of autonomy, it is necessary to know the needs of the latter. The human activity recognition is a way to know these needs by tracking the actions of residents using recognition algorithms and sensors placed in the home or on the individual. The most efficient human activity recognition algorithms are mainly based on supervised learning, which itself relies on the quality of the learning data collected in the residences. However, data collection is a time-consuming, costly and complex process, especially when we want to create a representative dataset that guarantees the performance and generalization capacity of the model that uses it. In order to provide an alternative to this data collection, this work presents an approach to human activity recognition based on data from the web. We show that exploiting images and text retrieved from the web with the right algorithm allows to obtain good recognition performances with an average accuracy of 0.91 for 5 activities of daily living.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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 source (direct Gemma or distilled Codex), 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".