Interpretable hand gesture recognition: a rule-based framework for radar-based gesture onset detection and classification
Bibliographic record
Abstract
Abstract Hand gesture recognition (HGR) has gained significant attention in human-computer interaction, enabling touchless control in various domains, such as virtual reality, automotive systems, and healthcare. While deep learning approaches achieve high accuracy in gesture classification, their lack of interpretability hinders transparency and user trust in critical applications. To address this, we extend MIRA, an interpretable rule-based HGR system, with a novel gesture onset detection method that autonomously identifies the start of a gesture before classification. Our onset detection approach achieves 90.13% accuracy on average, demonstrating its robustness across users. By integrating signal processing techniques, MIRA enhances interpretability while maintaining real-time adaptability to dynamic environments. Additionally, we introduce a background class, enabling the system to differentiate between gesture and non-gesture frames and expand the dataset with new users and recordings to improve generalization. We further analyze how feature diversity affects performance, showing that low diversity can suppress personalization due to early misclassifications. Using a foundational and personalized rule framework, our approach correctly classifies up to 94.9% of gestures, reinforcing the impact of personalization in rule-based systems. These findings demonstrate that MIRA is a robust and interpretable alternative to deep learning models, ensuring transparent decision-making for real-world radar-based gesture 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".