## 1 Introduction  
Many people and businesses have set as their ideal goal the systematic management of an individual's mental and physical health, and the identification of emotional states that are not directly expressed, and the provision of convenience and services accordingly. Artificial intelligence has recently attracted attention as an essential technology for realizing this ideal. The information obtained from human-like biological signals, facial expressions, voices, and behaviors can infer unexpressed health or expressions of opinion, thereby enabling an appropriate response even when people are physically challenged or unable to express their own intentions [1]. In particular, ECG signals from a person's heart contain high-quality information about the person which can be used to identify their health status and emotional status [2]. In general, in order to acquire an ECG signal, a professional technique needs to be followed, attaching pads and devices to the body without movement. However, in order to analyze ECG signals in everyday environments, pads and devices that work in a contact manner are not suitable [3]. Deep learning is a technology that classifiesand predicts using high-dimensional nonlinear features extracted from large-scale data, making it suitable for radars with high-frequency bands to pass through the human body and analyze the reflected signals to acquire ECG information. In order to rebuild an ECG in a non-contact manner through radar signals, there is a limit in terms of checking changes in the signal over time. When a high-frequency radar passes or is reflected through a human body, it is received, including the activity signal information performed therein. In this case, signal-processing technologies such as noise filters and heart rate extraction are applied by checking the frequency band that includes the desired information [4,5]. In order to check the frequency which changes over time, a method of analyzing signals in a high-dimensional manner by converting a one-dimensional radar signal into a two-dimensional time-frequency image is also being studied. A signal transformed into a two-dimensional format can be treated as an image, enabling the use of image preprocessing techniques and learning through CNN-based models, which are capable of handling a vast amount of embedded information. Existing models extract features from signals and transfer them to the FC layer, resulting in linear weights being applied, leading to output values. In contrast, fuzzy logic is applied to the input data using various membership functions (MFs), expressing the degree of membership for each one and generating rules based on these memberships, thereby creating linguistic rules which capture the relationships between feature channels. Although feature extraction abilities for large-scale data are somewhat lacking, this model can generate linguistic rules, enabling transparent learning and excellent processing abilities for uncertainty and ambiguous data acquired in the real world. In the case of the conventional fully connected (FC) layer, it is difficult to represent channel-specific relationships by the linear combination of high-dimensional nonlinear representations obtained from large-scale input data. As a result, the nonlinear features passing through the FC layer risk being weakened or lost, and the expressive power may decrease due to the flat combination approach. However, in the case of the anNFN layer, it usually has useful characteristics to solve these problems. This layer has the capability of handling complex nonlinear data and is advantageous for identifying relationships through clustering between input channels. Moreover, even if there is noise in the delivered feature map, it can obtain the robustness and stability of signal reconstruction. Thus, a deep-fuzzy method is being studied, combining two different methods to simultaneously use nonlinear feature extraction capabilities for large-scale data and processing capabilities for uncertain data [6].