# Emotion Estimation Using Noncontact Environmental Sensing with Machine and Deep Learning Models +
## 5 Discussion
Other researchers' results should also be considered, as comparing these results with those of this study can provide broader insights. For example, [PERSON] et al. demonstrated the effectiveness of ensemble learning algorithms over single classifiers for ECG-based emotion recognition, particularly showing that ensemble learning using decision trees achieves the highest accuracy [19]. This result aligns with the performance of the methods used in this study, confirming that similarly promising results have been obtained. As demonstrated by [PERSON] et al., deep learning models such as the LSTM model and the CNN-LSTM model have achieved high accuracy in EEG-based emotion recognition [21]. Additionally, [PERSON] et al. have shown that the CNN-LSTM model exhibits high accuracy in emotion recognition using environmental data [13]. These results suggest that deep learning models enhance emotion recognition accuracy by leveraging their strong ability to handle time-series data. Therefore, it is suggested that improving the CNN-LSTM model in the context of this study may make deep learning techniques effective for emotion recognition in environmental data as well. Valence estimation achieved higher accuracy, likely due to the significant impact of environmental factors such as room comfort and surrounding conditions. These factors directly influence emotional states associated with valence, making them easier to estimate from environmental data alone. In contrast, arousal estimation appears to be more challenging, as it may be influenced by additional factors such as individual psychological states and temporal variations in behavior or activity levels. For example, changes in energy levels throughout the day or external stressors may contribute to the observed difficulty in accurate prediction. To improve arousal estimation within the scope of non-invasive environmental data, future efforts could focus on exploring new environmental metrics and refining existing analyses. For instance, while factors such as illuminance and noise levels are already measured, delving into detailed characteristics such as noise frequency spectra may uncover additional patterns relevant to emotional arousal. Similarly, introducing additional air quality metrics beyond CO2 and Odor, such as particulate matter (PM2.5), might enhance the model's ability to capture subtle environmental influences. Moreover, incorporating temporal dynamics--such as diurnal patterns or activity-based time segmentation--could provide deeper insights into the interaction between environmental conditions and arousal over time, leading to improved prediction accuracy. This experiment has several limitations. For example, the data collection period was limited to September and October, and the sample size was only seven participants. To gain more generalizable insights, a more extended data collection period and sample size are necessary. Additionally, data for other methods were not collected, so a comparison of accuracy could not be achived. Future research should compare the proposed method with other models to clarify its relative performance. By further developing the insights gained from this study, it is expected that emotion estimation technologies will become more practical for implementation in workplaces and homes. In particular, collaboration with IoT technologies is anticipated to lead to the development of devices capable of real-time environmental data collection and automatic adjustment. In workplaces, real-time monitoring of employees' stress and emotional states will enable immediate environmental adjustments, leading to stress reduction and improved work efficiency. In homes, smart devices will be controlled according to changes in residents' emotions and comfort, providing a more comfortable and healthy living environment. As a result, emotion estimation technologies have the potential to be widely applied as automated support in daily life.