This paper introduces a lightweight gesture recognition system based on 60
GHz frequency modulated continuous wave (FMCW) radar. We show that gestures can
be characterized efficiently by a set of five features, and propose a slim
radar processing algorithm to extract these features. In contrast to previous
approaches, we avoid heavy 2D processing, i.e. range-Doppler imaging, and
perform instead an early target detection - this allows us to port the system
to fully embedded platforms with tight constraints on memory, compute and power
consumption. A recurrent neural network (RNN) based architecture exploits these
features to jointly detect and classify five different gestures. The proposed
system recognizes gestures with an F1 score of 98.4% on our hold-out test
dataset, it runs on an Arm Cortex-M4 microcontroller requiring less than 280 kB
of flash memory, 120 kB of RAM, and consuming 75 mW of power.

基于 60 GHz FMCW 雷达的轻量级手势识别系统，通过一种瘦身的雷达处理算法提取五个特征，并利用基于循环神经网络的架构实现手势的检测和分类。该系统在嵌入式平台上运行，对内存、计算和功耗有严格要求，达到了 98.4% 的 F1 分数，在 Arm Cortex-M4 微控制器上仅需 280 kB 的闪存空间、120 kB 的 RAM 和 75 mW 的功耗。