在航天健康传感器上实现低功耗心音信号实时分析。
At the Edge of the Heart: ULP FPGA-Based CNN for On-Device Cardiac Feature Extraction in Smart Health Sensors for Astronauts
- 用量化训练+阵列加速，实现纯整数推理。
- 98%准确率，仅耗电8.55毫瓦，95.5毫秒完成推理。
- 适合长期太空任务中的自主健康监测。
人类深空探索计划与地面健康监测需求的融合，推动了对极低资源约束可穿戴设备上可靠、实时特征提取的迫切要求。本文提出一种基于超低功耗（ULP）现场可编程门阵列（FPGA）的实时心音图（SCG）特征分类方案，采用卷积神经网络（CNN）。该方法结合量化感知训练与脉动阵列加速器，在Lattice iCE40UP5K FPGA上实现纯整数推理，该平台因功耗低和抗辐射能力强，特别适用于电池供电的太空环境部署。系统验证准确率达98%，功耗仅为8.55 mW，推理时间95.5毫秒，仅需2,861个LUT和7个DSP模块。结果表明，在资源受限硬件上实现完全本地化的心脏特征提取是可行的，为长期太空任务中高效、自主的健康监测提供了可能。
原文摘要 · Abstract (English)
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.
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