Fine-Grained Human Activity Recognition in Smart Homes Through Photoplethysmography-Based Hand Gesture Detection
Bibliographic record
Abstract
The aging population, coupled with medical staff shortages, has become a significant challenge in most developed countries. It is increasingly evident that the necessity to maintain elders at home cannot be overlooked. To ensure their well-being and safety, the concept of smart homes emerges as a promising solution, monitoring their daily activities and providing support when necessary. In scientific literature, the focus has predominantly been on monitoring high-level activities such as eating, walking, or sleeping. However, for identifying erroneous executions, a system capable of recognizing fine-grained specific steps of a task in real time is required. In this paper, we propose a novel algorithmic approach for fine-grained activity recognition, utilizing photoplethysmography sensors. The machine learning approach leverages a dataset collected from a wristband equipped with an accelerometer and photoplethysmography sensors. To construct this dataset, we defined a series of atomic cooking gestures performed by participants. The collected data have been labeled and will be made available to the scientific community. We achieved promising results, with an accuracy of 94%, demonstrating the potential of photoplethysmography sensors in smart homes for assistive activity recognition, a domain where they have been rarely utilized.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".