Learning Interpretable Rules from Neural Networks: Neurosymbolic AI for Radar Hand Gesture Recognition

TL;DR

This paper introduces RL-Net, a neuro-symbolic neural network that learns interpretable rules for radar-based hand gesture recognition, balancing accuracy and transparency, and demonstrating practical applicability for edge sensing systems.

Contribution

The work presents RL-Net, a novel neuro-symbolic rule learning model for radar HGR, addressing interpretability challenges and benchmarking against existing transparent and black-box models.

Findings

RL-Net achieves 93.03% F1 score, balancing performance and interpretability.

Identifies optimization challenges in rule pruning and hierarchy bias.

Proposes modifications to enhance stability and practical deployment.

Abstract

Rule-based models offer interpretability but struggle with complex data, while deep neural networks excel in performance yet lack transparency. This work investigates a neuro-symbolic rule learning neural network named RL-Net that learns interpretable rule lists through neural optimization, applied for the first time to radar-based hand gesture recognition (HGR). We benchmark RL-Net against a fully transparent rule-based system (MIRA) and an explainable black-box model (XentricAI), evaluating accuracy, interpretability, and user adaptability via transfer learning. Our results show that RL-Net achieves a favorable trade-off, maintaining strong performance (93.03% F1) while significantly reducing rule complexity. We identify optimization challenges specific to rule pruning and hierarchy bias and propose stability-enhancing modifications. Compared to MIRA and XentricAI, RL-Net emerges as a practical middle ground between transparency and performance. This study highlights the real-world feasibility of neuro-symbolic models for interpretable HGR and offers insights for extending explainable AI to edge-deployable sensing systems.