Rahman, Kazi Mohammad AbidurKazi Mohammad AbidurRahmanAlbrecht, Urs-VitoUrs-VitoAlbrechtKulau, UlfUlfKulau2026-03-132026-03-13202522. GI/ITG KuVS Fachgespräch Sensornetze, FGSN 2025https://hdl.handle.net/11420/62110Reliable labeling of systolic and diastolic events in time-series seismocardiography (SCG) signals presents significant challenges due to morphological variability and susceptibility to motion artifacts. This research addresses this critical limitation through a novel signal processing framework leveraging triaxial SCG acquisition (SCGx, SCGy , SCGz ) and wavelet-based denoising. Our approach integrates multi-level Discrete Wavelet Transform decomposition for physiological band isolation, adap- tive coefficient thresholding for noise-robust feature preservation, and Inverse DWT reconstruction with moving-average filtering to extract systolic/diastolic envelopes. By exploiting inherent axis redundancy through cross-channel consensus and using synchro- nized ECG for cycle-level timing reference, we achieve robust automated segmentation of cardiac windows under real-world noise conditions. The resulting high-confidence labels establish a foundation for training resource-efficient neural networks to extract complex mechanical biomarkers directly at the sen- sor—advancing wearable cardiac monitoring beyond electrical activity toward precise mechanical function quantification.enTechnology::600: TechnologyConference Paper10.21268/20250819-0Conference Paper