Interpretable Multisensor-Based Hand Gesture Detection for Fine-Grained Activity Recognition
Bibliographic record
Abstract
To explore the feasibility of enhancing independence among the aging population through wearable sensing technologies, this preliminary study presents and evaluates a novel system for fine-grained human activity recognition in smart home environments. Conducted at the Université du Québec à Chicoutimi with volunteer students and personnel aged between 20 and 45 years, the study investigated the use of photoplethysmography (PPG) and accelerometer sensors to identify six cooking gestures. The proposed model employed Spatial Dilated Convolution (SDC) encoders to capture local temporal patterns, achieving up to 95% accuracy in gesture detection. By comparing the attention weights of a Multivariate Multi-Head Attention (MMA) mechanism with the gate strengths of a Focal Modulation (FM) mechanism, we further assessed their interpretability in identifying hand-specific contributions without explicit annotations, obtaining an 83% accuracy in dominant hand identification. Validation through a Leave-One-Group-Out evaluation across participants resulted in mean accuracy scores of 82% for gesture detection and 75% for dominant hand identification. These findings demonstrate the potential of repurposing PPG sensors to detect subtle, movement-induced signals as a proof of concept for a non-invasive and privacy-conscious monitoring approach. While preliminary, this work lays the groundwork for future studies targeting real-time, personalized assistance systems to promote elderly well-being in smart home contexts.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".