Interpretable Rule-Based System for Radar-Based Gesture Sensing: Enhancing Transparency and Personalization in AI

TL;DR

This paper introduces MIRA, an interpretable rule-based system for radar gesture detection that improves transparency and personalization, demonstrating high performance and interpretability in safety-critical applications.

Contribution

The paper presents MIRA, a novel multi-class rule-based algorithm for radar gesture sensing that enhances interpretability and personalization compared to existing methods.

Findings

MIRA achieves high accuracy in radar gesture classification.

The system provides clear decision explanations, improving user trust.

Personalized rule sets adapt to individual user behaviors.

Abstract

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.