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

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.

Content

Subjects

CNN

FPGA

SCG

Space Wearables

Systolic Array

Time-Series-Classification

Ultra-Low-Power

DDC Class

600: Technology

Funding References

Funding(s)

SCG-Sensorsystem für Artemis II Experiment SpacePatch Teilvorhaben I: Signalverarbeitungskette und lokale Datensicherung auf SCG-Sensorsystem

Kompensierte Ortsvektoren zur Charakterisierung seismokardiografischer Signale mittels integrierter Sensorik