# Emotion Estimation Using Noncontact Environmental Sensing with Machine and Deep Learning Models +
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noncontact environmental sensors. The environmental big data thus collected are used to construct models for emotion estimation by employing machine learning and deep learning techniques. This approach avoids the need for wearable devices or cameras in conducting such research, thereby addressing issues related to stress and privacy. In previous studies, emotions are predominantly treated as categorical data (Relaxed, Sad, Angry, and Happy) and addressed as a classification problem, where the goal was to assign each data point to one of the predefined categories. In contrast, this study represents emotions using two continuous dimensions: Arousal and Valence. These dimensions capture both the type and intensity of emotional states, enabling a more nuanced understanding compared to the categorical approach. The proposed method utilizes physiological sensor data during the model training phase to estimate Arousal and Valence values. However, after training, it estimates emotions using only environmental sensor data. The proposed method, similar to previous research, uses physiological sensor data for emotion data during the model training phase. However, after training, it estimates emotions using only environmental sensor data. With the rapid advancement of Internet of Things (IoT) technology, many household devices have become connected to the internet, leading to the widespread adoption of smart home technologies. These devices are equipped with environmental sensors such as temperature, humidity, CO\({}_{2}\) concentration, and light intensity, and the real-time environmental data collected from these sensors can now be easily gathered and analyzed online. Furthermore, by utilizing devices such as air conditioners, air purifiers, and humidifiers, it is possible to adjust the indoor environment sensors. For example, by estimating emotions in real time and adjusting the room temperature or exchanging the air based on the results, it is possible to provide an optimal environment for each individual's psychological state. Emotion estimation technology using environmental data can be easily implemented in both homes and workplaces, offering significant potential to greatly improve the quality of daily life. To validate the effectiveness of estimating Arousal and Valence using environmental data, we prepared an experimental facility and conducted experiments to collect both environmental and emotional data. Using these data, we constructed models for emotion estimation employing random forest (RF), gradient-Boosting decision trees (GBDT), and cnvolutional neural network-long short-term memory (CNN-LSTM). We evaluated the accuracy of models using the coefficient of determination (R\({}^{2}\)). The objective of this study is to discuss the feasibility of estimating emotions such as arousal and valence based on environmental data through experiments, and to propose a model that can estimate emotions with high accuracy. This method is non-invasive, as it does not require wearable devices, and it also does not require cameras, thus addressing privacy concerns in emotion estimation. Such emotion estimation methods are easier to implement as services in general businesses, households, and educational settings, potentially expanding the range of people who can benefit from emotion estimation. Furthermore, emotion estimation models based on environmental data have the potential to identify environmental factors that influence emotions. This not only deepens the understanding of emotions but also enables environmental adjustments in real life according to emotional states, making a significant contribution to practical applications. The structure of this paper is as follows. Section 2 reviews previous research on emotion estimation and discusses the relationship between environmental factors and emotions.