用类脑雷达实现低功耗实时手势识别，仅在有动作时触发计算。
Live Demonstration: Neuromorphic Radar for Gesture Recognition
- 通过异步Sigma-Delta编码将雷达信号转为稀疏脉冲流
- 在七人五手势数据集上实现实时识别准确率超85%
- 适合嵌入式设备，特别适用于低功耗场景
我们提出一种类脑雷达框架，用于实时、低功耗的手势识别（HGR），采用受生物感知启发的事件驱动架构。系统包含24 GHz多普勒雷达前端和定制的类脑采样器，将中频（IF）信号通过异步Sigma-Delta编码转换为稀疏脉冲表示。这些事件直接由部署在Cortex-M0微控制器上的轻量神经网络处理，实现低延迟推理，无需进行谱图重构。与传统连续采样处理的雷达手势识别流程不同，本架构仅在检测到有意义运动时激活，显著降低内存、功耗和计算开销。在7名用户采集的5种手势数据集上评估，系统实现超过85%的实时识别准确率。据我们所知，这是首个将生物启发的异步Sigma-Delta编码与事件驱动处理框架应用于雷达手势识别的工作。
原文摘要 · Abstract (English)
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.
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