Abstract
We present a neuromorphic radar framework for real-time, low-power hand gesture recognition (HGR) using an event-driven architecture inspired by biological sensing. Our system comprises a 24 GHz Doppler radar front-end and a custom neuromorphic sampler that converts intermediate-frequency (IF) signals into sparse spike-based representations via asynchronous sigma-delta encoding. These events are directly processed by a lightweight neural network deployed on a Cortex-M0 microcontroller, enabling low-latency inference without requiring spectrogram reconstruction. Unlike conventional radar HGR pipelines that continuously sample and process data, our architecture activates only when meaningful motion is detected, significantly reducing memory, power, and computation overhead. Evaluated on a dataset of five gestures collected from seven users, our system achieves > 85% real-time accuracy. To the best of our knowledge, this is the first work that employs bio-inspired asynchronous sigma-delta encoding and an event-driven processing framework for radar-based HGR.
Abstract (translated)
我们提出了一种基于生物感知启发的事件驱动架构，用于实时、低功耗的手势识别（HGR）的神经形态雷达框架。我们的系统包括一个24 GHz多普勒雷达前端和一个定制的神经形态采样器，该采样器通过异步Sigma-Delta编码将中频(IF)信号转换为稀疏的基于脉冲(Spike)表示形式。这些事件由部署在Cortex-M0微控制器上的轻量级神经网络直接处理，从而能够在不进行光谱图重建的情况下实现低延迟推理。与传统的雷达HGR流水线连续采样和处理数据不同，我们的架构仅在检测到有意义的运动时才激活，这大大减少了内存、功耗和计算开销。 在一个包含七名用户采集五种手势的数据集上评估后，我们的系统实现了超过85%的实时准确率。据我们所知，这是第一个采用生物启发式异步Sigma-Delta编码和事件驱动处理框架用于雷达基手势识别的工作。
URL
https://arxiv.org/abs/2508.03324
