Non-Contact Heart Rate Detection Using FMCW Radar Based on 1-D Convolutional Neural Networks - Diyah Widiyasari; Istiqomah Istiqomah; Fiky Yosef Suratman; Suto Setiyadi (Journal of Electronics, Electromedical Engineering, and Medical Informatics, 2026)
Non-contact heart rate (HR) estimation using frequency-modulated continuous-wave (FMCW) radar has emerged as a promising solution for unobtrusive, continuous vital-sign monitoring. However, accurately extracting HR from radar signals remains challenging because of low-amplitude cardiac-induced chest vibrations, environmental clutter, motion artifacts, and system noise. Traditional signal processing techniques, such as bandpass filtering combined with fast Fourier transform (FFT) analysis, are commonly employed to estimate HR in the frequency domain. Nevertheless, these approaches are highly sensitive to noise and often struggle to robustly capture weak cardiac components, leading to unstable or inaccurate estimates. To address these limitations, this study proposes a non-contact HR estimation framework based on FMCW radar combined with a one-dimensional convolutional neural network (1D-CNN). A systematic radar signal preprocessing pipeline is developed, including range-bin selection, phase extraction, noise suppression, filtering, and structured data labeling, to construct learning-ready input features. The 1D-CNN model is designed to automatically learn discriminative temporal patterns associated with cardiac activity directly from preprocessed radar signals. The proposed method is evaluated using two datasets: a publicly available dataset and an independently acquired dataset collected under controlled conditions. Performance is benchmarked against conventional bandpass filtering- and FFT-based HR estimation methods. The experimental results demonstrate that the proposed 1D-CNN framework achieves more accurate and stable HR predictions. On the public dataset, MAE decreases from 17.96 to 6.09 BPM, RMSE from 21.28 to 7.34 BPM, and MedAE from 17.66 to 5.43 BPM. The independent dataset yields consistent gains, with MAE decreases from 14.05 to 5.45 BPM, RMSE from 18.05 to 6.84 BPM, and MedAE from 10.74 to 4.57 BPM. These results indicate that the proposed 1D-CNN framework can effectively estimate HR from radar signals and demonstrate its capability to operate across datasets acquired with different radar frequencies