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At the edge of the heart: ULP FPGA-based CNN for on-device cardiac feature extraction in smart health sensors for astronauts
Citation Link: https://doi.org/10.15480/882.17037
Publikationstyp
Conference Paper
Date Issued
2026
Sprache
English
TORE-DOI
Citation
22nd Annual International Conference on Distributed Computing in Smart Systems and the Internet of Things, DCOSS-IoT 2026
Peer Reviewed
true
The convergence of accelerating human spaceflight ambitions and critical terrestrial health monitoring demands is driving the unprecedented requirements for reliable and real-time feature extraction capabilities on extremely resource-constraint wearable health sensors. We present a ULP 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 – for their power efficiency and radiation resilience. The implementation achieves a validation accuracy of 98% while consuming only 8.55 mW of power, 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.
Subjects
FPGA
Ultra-Low-Power
CNN
Systolic Array
Time-Series-Classification
SCG
Space Wearables
DDC Class
610: Medicine, Health
621.38: Electronics, Communications Engineering
610: Medicine, Health
006.32: Neural Networks
Funding(s)
Funding Organisations
Publication version
acceptedVersion
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Name
SMS_NNarray.pdf
Type
Main Article
Size
2.13 MB
Format
Adobe PDF