MIRA用可解释规则实现雷达手势识别，提升安全场景信任度
Interpretable Rule-Based System for Radar-Based Gesture Sensing: Enhancing Transparency and Personalization in AI
- 基于规则的多分类架构，决策过程透明可读
- 支持个性化规则定制，适配不同用户行为特征
- 在雷达手势数据集上表现优异，适合高安全要求场景
人工智能对既高效又可解释的模型需求日益增长，尤其在安全与信任至关重要的领域。本文提出MIRA——一种面向雷达手势识别的透明、可解释的多类别规则系统。针对可解释AI的关键需求，MIRA通过提供决策过程洞察增强用户信任，并展示其通过个性化规则集适配个体行为的灵活性，实现以用户为中心的AI体验。我们不仅提出一种新颖的多类别分类架构，还公开了一个大规模调频连续波雷达手势数据集，并通过对比分析验证了系统的卓越可解释性。研究证实MIRA兼具高可解释性与良好性能，强调了可解释AI在安全关键应用中更广泛采用的潜力。
原文摘要 · Abstract (English)
The increasing demand in artificial intelligence (AI) for models that are both effective and explainable is critical in domains where safety and trust are paramount. In this study, we introduce MIRA, a transparent and interpretable multi-class rule-based algorithm tailored for radar-based gesture detection. Addressing the critical need for understandable AI, MIRA enhances user trust by providing insight into its decision-making process. We showcase the system's adaptability through personalized rule sets that calibrate to individual user behavior, offering a user-centric AI experience. Alongside presenting a novel multi-class classification architecture, we share an extensive frequency-modulated continuous wave radar gesture dataset and evidence of the superior interpretability of our system through comparative analyses. Our research underscores MIRA's ability to deliver both high interpretability and performance and emphasizes the potential for broader adoption of interpretable AI in safety-critical applications.
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