## 2 Related Work  
This section describes the deep learning techniques applied to acquire vital sign signals through existing external devices and convert them into an ECG or heart rate. The signal obtained by being reflected from the body through the radar includes information on the heart rate. It aims to import information related to the heart rate by frequency decomposition or pass through a deep network or algorithm and generate the desired ECG signal. [PERSON] [7] applied ANFN and unspecified wavelet transform to extract fetal ECGs from maternal ECGs. This technique is useful for handling nonlinear and time-series data, recording lower MSE values than discrete wavelet transforms. [PERSON] [8] applied a lightweight residual U-Net to pursue simple ECG measurement monitoring through a millimeter-wave (MW) radar. The experiment was carried out and verified with data measured using 10 subjects. As a result, the reconstructed ECG signal and actual signal showed a correlation coefficient of 0.8 or higher and proved the effect. [PERSON] [9] converted radar and ECG data obtained from 30 subjects into spectrograms through short-time Fourier transforms and then conducted signal reconstruction research with RSSRnet. As a result of verifying the performance of the signal converted through cross-channel attention in the model through the median-normalized root mean square error, a value of 0.049 was shown. [PERSON] [10] designed an end-to-end DNN model to rebuild a non-contact ECG signal with an MW radar system. Signal reconstruction via a deep network by extracting four-dimensional cardiac motion has a median error of 14 ms or less of the electrical event of the heart, and the results are significantly similar to real ECGs with a median Pearson correlation coefficient of 90%. [PERSON] [11] reconstructed it into a deep network through signal-processing algorithms and radio frequency signals based on signals acquired in four different situations through 35 experiments. The experimental results confirmed that the dimensional transformation algorithm and the deep network are effective in transforming signals. [PERSON] [12] conducted a study to receive signals from outside the body with a frequency-modulated continuous wave and rebuild them into ECG signals through CNN. In six adult males without heart disease, the reconstruction performance was confirmed with signals obtained in a contactless or contact manner. Although noise was added, the signal reconstruction method using deep learning was confirmed to have good performance with a low signal-to-noise ratio. [PERSON] [13] conducted a wavelet-scattering network-long short-term memory (WSN-LSTM) network study that enabled the measurement of the ECG signal and heart rate via FMCW radar signals using data from Physio Net. The bio signal used variational-mode extraction to extract the heart rate signal and built the heart rate signal with the learned WSN-LSTM. Afterwards, when the heart rate was estimated by fast Fourier transform, a lower error was recorded compared to the conventional method. [PERSON] [14]'s study aimed to achieve contactless heart monitoring through Doppler data sensors. DCG signals and deep networks were utilized to generate ECG signals containing a heart rate variability similar to ECG information, rather than diagnosing through conventional ECG alone. For data processing, they used a variational autoencoder network and found a 58% improvement in consistency. [PERSON] [15] proposed an ECG reconstruction model that combined CNN and LSTM to measure the heart rate in a non-contact manner through Doppler sensors. For verification, data from nine healthy subjects without heart disease were used, and the correlation coefficient between the reconstructed signal and the actual signal was 0.86, confirming a similar performance. [PERSON] [16] used a deep learning method as a way to reconstruct ECG signals from photoplethysmogram signals.