Rahman, Kazi Mohammad AbidurKazi Mohammad AbidurRahmanRakhshan, DavisDavisRakhshanLütke, PhilippPhilippLütkeHarms, LauraLauraHarmsKulau, UlfUlfKulau2026-09-152026-09-152026-0622nd Annual International Conference on Distributed Computing in Smart Systems and the Internet of Things, DCOSS-IoT 2026https://hdl.handle.net/11420/64862The 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.enCNNFPGASCGSpace WearablesSystolic ArrayTime-Series-ClassificationUltra-Low-PowerTechnology::600: TechnologyConference Paper10.1109/DCOSS-IoT69657.2026.00019