Rahman, Kazi Mohammad AbidurKazi Mohammad AbidurRahmanRakhshan, DavisDavisRakhshanLütke, PhilippPhilippLütkeHarms, LauraLauraHarmsKulau, UlfUlfKulau2026-05-042026-05-04202622nd Annual International Conference on Distributed Computing in Smart Systems and the Internet of Things, DCOSS-IoT 2026https://hdl.handle.net/11420/62895The 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.enhttps://creativecommons.org/licenses/by/4.0/FPGAUltra-Low-PowerCNNSystolic ArrayTime-Series-ClassificationSCGSpace WearablesTechnology::610: Medicine, HealthTechnology::621: Applied Physics::621.3: Electrical Engineering, Electronic Engineering::621.38: Electronics, Communications EngineeringTechnology::610: Medicine, HealthComputer Science, Information and General Works::006: Special computer methods::006.3: Artificial Intelligence::006.32: Neural NetworksConference Paperhttps://doi.org/10.15480/882.1703710.15480/882.17037