ID 2360: Improving the robustness of radar-based heart sound detection
Monitoring vital signs is crucial for assessing the physiological status of individuals. The heart rate is a vital sign typically measured continuously using wearables or electrocardiogram (ECG) recording. Contactless vital sign monitoring is desirable in various healthcare settings to avoid the inconvenience and discomfort of traditional contact-based sensing . Continuous wave Doppler radar (CWDR) is a promising approach for non-contact measurement of heartbeats as it measures the relative displacement of the human body caused by the contraction of the heart muscle [2, 3], and radiofrequency waves can penetrate clothing and bedding.
As Will et al. and Shi et al. [3, 4] show, heartbeat information can be derived from the received radar echo by detecting two characteristic heart sounds occurring during a single heartbeat. The mechanical cause of these sounds is, with respect to the underlying heart rate, high-frequent with spectral components in the recorded radar echo in the band of 16-80 Hz . However, existing processing pipelines for detecting heart sounds must be robust against random large body movements (RLBMs) which are an integral part of human behavior. A recent study by Herzer et al.  has shown that controlled large-body movements significantly impact the accuracy of these approaches due to the noise they induce in the 16-80 Hz frequency band.
To address this limitation, this thesis aims to build up on the method developed by Shi et al.  which has proven itself in stationary settings and improved its performance on noisy data. The goal is to derive a heart sound detection algorithm that is applicable in real-life scenarios by accounting for RLBMs more precisely. In addition, since currently published datasets containing radar and reference signals, such as , do not contain RLBMs, this thesis aims to collect and curate a more realistic dataset for testing novel radar-based heart sound detection pipelines. Combining multiple datasets from sleep and stress research will be used in this thesis to enhance the generalizability & robustness of existing models. The state-of-the-art processing pipelines [3, 4] will be improved stepwise using different approaches, such as retraining the pipeline on the new dataset containing RLBMs, choosing different pre-processing approaches, modifying the network architecture, combining the information of multiple radar nodes, etc.
 Wenjin Wang, Xuju Wang, “Contactless Vital Signs Monitoring”, 2021, pp. 1-24, https://doi.org/10.1016/C2019-0-04343-9.
 Hu, Z. Zhao, Y. Wang, H. Zhang and F. Lin, “Noncontact Accurate Measurement of Cardiopulmonary Activity Using a Compact Quadrature Doppler Radar Sensor,” in IEEE Transactions on Biomedical Engineering, vol. 61, no. 3, pp. 725-735, March 2014, doi: 10.1109/TBME.2013.2288319.
 Will, C., Shi, K., Schellenberger, S. et al. Radar-Based Heart Sound Detection. Sci Rep 8, 11551 (2018). https://doi.org/10.1038/s41598-018-29984-5.
 Shi et al., “Segmentation of Radar-Recorded Heart Sound Signals Using Bidirectional LSTM Networks,” 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin, Germany, 2019, pp. 6677-6680, doi: 10.1109/EMBC.2019.8857863.
 Holldack, K. & Wolf, D. Atlas und kurzgefaßtes Lehrbuch der Phonokardiographie und verwandter Untersuchungsmethoden. (Thieme, 1966).
 Herzer et al., “Influence of Sensor Position and Body Movements on Radar-Based Heart Rate Monitoring,” 2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), Ioannina, Greece, 2022, pp. 1-4, doi: 10.1109/BHI56158.2022.9926775.
 Shi, K., Schellenberger, S., Will, C. et al. A dataset of radar-recorded heart sounds and vital signs including synchronised reference sensor signals. Sci Data 7, 50 (2020). https://doi.org/10.1038/s41597-020-0390-1.