Lu, HuiHuiLuHeyder, MarkusMarkusHeyderWenzel, MarvinMarvinWenzelAlbrecht, Nils ChristianNils ChristianAlbrechtLanger, DominikDominikLangerKölpin, AlexanderAlexanderKölpin2023-03-202023-03-202023-01IEEE Radio and Wireless Symposium (RWS 2023)http://hdl.handle.net/11420/15022Heart rate is one of the most critical and important vital signs in healthcare. While electrocardiography (ECG) is gold-standard procedure for heart rate monitoring, contactless monitoring is preferred in many applications like long-term monitoring. Radar systems enable contactless sensing by measuring small movements on the chest induced by the heart beat. In this paper, we present a machine learning-based method using a bidirectional gated recurrent unit (bi-GRU) network for accurate heartbeat detection. Band-pass filtered in-phase (I) and quadrature (Q) signals in heart sound and pulse wave frequency ranges were fused. The proposed method achieves a high F1 score of 98.06% for heart beat detection, thus outperforming the state-of-the-art method with an F1 score of 95.62% in the resting scenario. In the tilt-up scenario with the tilt table, F1 score is significantly improved by 10%. Besides, a median inter-beat intervals (IBIs) RMSE of only 22.07 ms in the resting scenario is realized.encontinuous wave radargated recurrent unitheart rate monitoringmachine learningsignal processingMLE@TUHHChemieTechnikTechnische ChemieConference Paper10.1109/RWS55624.2023.10046202Conference Paper