TUHH Open Research
 

At the edge of the heart: ULP FPGA-based CNN for on-device cardiac feature extraction in smart health sensors for astronauts

Start Page
96
End Page
104
Citation
22nd Annual International Conference on Distributed Computing in Smart Systems and the Internet of Things, DCOSS-IoT 2026
Contribution to Conference
22nd Annual International Conference on Distributed Computing in Smart Systems and the Internet of Things, DCOSS-IoT 2026  
Publisher DOI
10.1109/DCOSS-IoT69657.2026.00019
Scopus ID
2-s2.0-105049643994
Publisher
IEEE
ISBN of container
979-8-3315-4670-0
979-8-3315-4671-7
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
CNN
FPGA
SCG
Space Wearables
Systolic Array
Time-Series-Classification
Ultra-Low-Power
DDC Class
600: Technology