HACER: An Integrated Remote Monitoring Platform for the Elderly
Bibliographic record
Abstract
Recognizing human actions and emotions using video analysis has great potential for improving the quality of life for the elderly. However, current datasets used for the recognition typically focus on either emotion recognition or action recognition, limiting the scope of research investigating the interdependence between actions and emotions. To address this limitation, we present an end-to-end process for jointly performing human action and emotion recognition by simultaneously extracting action-specific and emotion-specific features from video input. Our proposed approach aims to develop and test machine learning models for recognizing human actions and emotions in smart environments for the elderly to monitor remotly. Additionally, we propose the Human ACtion and Emotion Recognition dataset, HACER, a unique dataset that jointly incorporates both emotion and action labels, providing valuable insights into the interplay between emotions and actions. Our proposed dataset fills a crucial gap in the existing literature, enabling the development of machine learning models that can recognize and classify both action and emotion categories at once, advancing the field of multimodal recognition with only one sensor. The dataset is publicly available at: https://www.kaggle.com/datasets/siwarammar/hacer-human-action-and-emotion-recognition
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".