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...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/2/721 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832589213340532736 |
|---|---|
| author | Tsumugi Isogami Nobuyoshi Komuro |
| author_facet | Tsumugi Isogami Nobuyoshi Komuro |
| author_sort | Tsumugi Isogami |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-b57f6f1186314211b3f8a1f96c0b9db3 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-b57f6f1186314211b3f8a1f96c0b9db32025-01-24T13:20:35ZengMDPI AGApplied Sciences2076-34172025-01-0115272110.3390/app15020721Emotion Estimation Using Noncontact Environmental Sensing with Machine and Deep Learning ModelsTsumugi Isogami0Nobuyoshi Komuro1Graduate School of Science and Engineering, Chiba University, Chiba 263-8522, JapanDigital Transformation Enhancement Council, Chiba University, Chiba 263-8522, JapanThis 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.https://www.mdpi.com/2076-3417/15/2/721emotion estimationnon-contact environmental sensingmachine learningdeep learningwireless sensor networks (WSN) |
| spellingShingle | Tsumugi Isogami Nobuyoshi Komuro Emotion Estimation Using Noncontact Environmental Sensing with Machine and Deep Learning Models Applied Sciences emotion estimation non-contact environmental sensing machine learning deep learning wireless sensor networks (WSN) |
| title | Emotion Estimation Using Noncontact Environmental Sensing with Machine and Deep Learning Models |
| title_full | Emotion Estimation Using Noncontact Environmental Sensing with Machine and Deep Learning Models |
| title_fullStr | Emotion Estimation Using Noncontact Environmental Sensing with Machine and Deep Learning Models |
| title_full_unstemmed | Emotion Estimation Using Noncontact Environmental Sensing with Machine and Deep Learning Models |
| title_short | Emotion Estimation Using Noncontact Environmental Sensing with Machine and Deep Learning Models |
| title_sort | emotion estimation using noncontact environmental sensing with machine and deep learning models |
| topic | emotion estimation non-contact environmental sensing machine learning deep learning wireless sensor networks (WSN) |
| url | https://www.mdpi.com/2076-3417/15/2/721 |
| work_keys_str_mv | AT tsumugiisogami emotionestimationusingnoncontactenvironmentalsensingwithmachineanddeeplearningmodels AT nobuyoshikomuro emotionestimationusingnoncontactenvironmentalsensingwithmachineanddeeplearningmodels |