Emotion Estimation Using Noncontact Environmental Sensing with Machine and Deep Learning Models
This paper presents a method for estimating arousal and emotional valence levels using non-contact environmental sensing, addressing challenges such as discomfort from long-term device wear and privacy concerns associated with facial image analysis. We employed environmental data to develop machine...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/2/721 |
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| Summary: | This paper presents a method for estimating arousal and emotional valence levels using non-contact environmental sensing, addressing challenges such as discomfort from long-term device wear and privacy concerns associated with facial image analysis. We employed environmental data to develop machine learning models, including Random Forest, Gradient Boosting Decision Trees, and the deep learning model CNN-LSTM, and evaluated their accuracy in estimating emotional states. The results indicate that decision tree-based methods, particularly Random Forest, are highly effective for estimating emotional states from environmental data. |
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| ISSN: | 2076-3417 |