Valence, on the other hand, refers to the positive or negative direction of moods and emotions, indicating whether they are positive or negative. [PERSON] [27] used a millimeter-wave radar to extract and separate heartbeat and respiration signals, manually extracting 27 physiological signal-related features as inputs for two classification models. The average recognition accuracy for the four emotions was 87 and 72.3% for the person-dependent and person-independent classification models, respectively. However, current radar-based emotion recognition methods require manual feature identification to improve accuracy and have not been optimized for a closed-cabin environment, thereby providing a gap for our research. In this paper, we hypothesize that millimeter-wave radar sensors can classify individuals' emotions by measuring their respiratory signals in conjunction with machine learning algorithms within a closed-cabin environment. To address the issue of emotion recognition for personnel in closed cabins, this study proposes a method using a millimeter-wave radar to collect respiration signals, combined with a machine-learning framework for emotion classification and recognition. First, we used a continuous-wave radar to measure the respiration signals of the participants in a closed-cabin laboratory while they watched videos designed to elicit different emotions. After processing, we obtained respiration waveforms corresponding to different emotional states. Next, we used a sparse autoencoder (SAE) to extract features from the waveforms. These features were then inputted into two support vector machines (SVMs) for arousal and valence classification. Our method was compared with the FaceReader(tm), a commercial emotion recognition device that uses audiovisual signals. The results indicate that our method is not only more suitable for deployment in closed cabins but also maintains accuracy and provides objective results. The overall research framework is illustrated in Figure 1.  
The contributions of this paper can be summarized as follows. First, we used a millimeter-wave radar to capture human respiration signals in a closed-cabin environment, which is noncontact and interference-free. Second, considering the characteristics of feature extraction and selection in machine-learning, SAEs were introduced to extract respiration features for emotion recognition. Third, the results of the experiment demonstrated that RSP signals can be effectively used for emotion recognition, eliminating the need for more complex physiological signals such as EEG [28]. Although machine-learning involves significant computational effort during model training, the time required for testing is minimal. Finally, we preliminarily validated that the system can be applied to closed-cabin environments. Overall, this study provides a foundation for the development of noncontact emotion recognition systems that can operate effectively in challenging environments, ensuring safety and efficiency during critical operations. The remainder of this paper is as follows. In Section 2, the working principle of a millimeter-wave radar is explained, the experimental process is described, and the signal preprocessing algorithms used before emotion classification are introduced. Then, the implementation of the classifier and the feature extraction process were described. Section 3 presents the experimental results and cross-validation methods. Section 4 provides a discussion of the results. Finally, Section 5 presents the conclusions.