The convergence of accelerating human spaceflight ambitions and critical terrestrial health monitoring demands is driving unprecedented requirements for reliable, real-time feature extraction on extremely resource-constrained wearable health sensors. We present an ultra-low-power (ULP) Field-Programmable Gate Array (FPGA) based solution for real-time Seismocardiography (SCG) feature classification using Convolutional Neural Networks (CNNs). Our approach combines quantization-aware training with a systolic-array accelerator to enable efficient integer-only inference on the Lattice iCE40UP5K FPGA, which offers an ideal platform for battery-powered deployments -- particularly in space environments -- thanks to its power efficiency and radiation resilience. The implementation achieves a validation accuracy of 98% while consuming only 8.55 mW, completing inference in 95.5 ms with minimal hardware resources (2,861 LUTs and 7 DSP blocks). These results demonstrate that fully on-device SCG-based cardiac feature extraction is feasible on resource-constrained hardware, enabling energy-efficient, autonomous health monitoring for astronauts in long-duration space missions.

翻译：人类太空探索雄心的加速与关键地面健康监测需求的融合，正推动对极端资源受限可穿戴健康传感器上可靠、实时特征提取的严苛要求。我们提出一种基于超低功耗现场可编程门阵列的解决方案，利用卷积神经网络实现实时心震图特征分类。该方法结合量化感知训练与脉动阵列加速器，在Lattice iCE40UP5K FPGA上实现高效整型推理——该平台凭借其功耗效率与辐射耐受性，成为电池供电部署（尤其太空环境）的理想选择。实现方案的验证准确率达98%，功耗仅8.55 mW，推理耗时95.5 ms，且硬件资源消耗极低（2,861个查找表与7个DSP模块）。结果表明，在资源受限硬件上实现完全设备端的心震图心脏特征提取可行性，可为长期太空任务的宇航员提供能效自主的健康监测。