# Emotion Estimation Using Noncontact Environmental Sensing with Machine and Deep Learning Models +
## 6 Conclusions  
In this study, we proposed a non-contact method for estimating emotional valence and arousal using environmental data and evaluated its effectiveness. This approach offers a novel contribution to emotion estimation research by addressing privacy concerns, discomfort caused by wearable devices, and the limitations of traditional emotion sensing methods.  
The experimental results demonstrated that valence estimation achieved high accuracy, with the RF model showing particularly strong performance. The satisfactory results from both the RF model and the GBDT model highlight the potential of environmental data as a promising approach for valence estimation. On the other hand, the relatively lower accuracy of arousal estimation suggests the need for further improvements, especially by exploring additional data sources and refining the models.  
This study highlights the potential of non-invasive methods for emotion estimation using environmental data. While combining environmental data with physiological signals might enhance accuracy, such approaches could compromise the non-invasive nature of the proposed method. Future work should focus on exploring additional environmental features or refining machine learning models to further improve accuracy within the scope of non-invasive methodologies. Additionally, by incorporating interpretability into the learning models, it is anticipated that the changes in physiological signals and the environmental factors influencing those changes could be analyzed. These findings hold promise for applications in various fields, such as architecture, environmental design, and preventive medicine.  
From an industrial perspective, the findings are relevant for sectors where real-time emotion estimation can improve user experience, such as healthcare, customer service, and automotive industries. The development of devices capable of real-time environmental data collection, enabled by IoT technologies, could allow emotion estimation systems to be deployed in everyday applications, leading to personalized and context-aware interactions.  
Conceptualization, N.K. and T.I.; methodology, N.K. and T.I.; software, T.I.; validation, T.I.; formal analysis, T.I.; investigation, T.I.; resources, N.K.; data curation, T.I.; writing--original draft preparation, T.I.; writing--review and editing, N.K. and T.I.; visualization, T.I.; supervision, N.K.; project administration, N.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board of Chiba University Graduate School of Medicine (protocol code M10032 and date of approval: 8 September 2021).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Data Availability Statement: Data will be made available on request.  
We wish to express our appreciation to [PERSON] and [PERSON] for their constructive suggestions on experimental design.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} IoT & Internet of Things \\ HRV & Heart Rate Variability \\ EEG & Electroencephalogram \\ RF & Random Forest \\ GBDT & Gradient Boosting Decision Tree \\ CNN & Convolutional Neural Network \\ LSTM & Long Short Term Memory \\ \end{tabular}