Comparative analysis of two methods in fine-grained activity recognition for ambient assisted living
Bibliographic record
Abstract
The swift progression of artificial intelligence, along with the rising demand for in-home assistance for individuals facing a loss of autonomy, has driven a significant increase in research within the domain of ambient assisted living (AAL). A key challenge challenge in developing assistive technologies in an AAL context concerns the automatic recognition of the ongoing user’s activity. Most existing approaches of Human Activity Recognition use a level of abstraction (low granularity) that is insufficient for developing efficient assistive technologies. The majority of them focus primarily on identifying broad categories of activities, such as eating or sleeping. While this identification is sufficient for monitoring general behavior, it does not enable providing practically meaningful real-time, actionable assistance. In this paper, we propose a comparative study of two novel algorithmic approaches for hand gesture recognition, intended to serve as core components of a fine-grained activity recognition model. To this end, we have defined 13 atomic hand gestures commonly used in cooking activities. The first model we introduce utilizes inertial data, collected from a standard wristband equipped with a triaxial accelerometer and gyroscope, and applies machine learning techniques for analysis. The second model is based on a less conventional approach, employing photoplethysmography sensors, which are rarely used for activity recognition. We detail the design and implementation of both approaches and the conducted experiments. Finally, we present a comparative analysis of the obtained results showing the potential of such approaches for the AAL.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".