Emotion recognition with multiple physiological parameters based on ensemble learning
Abstract Emotion recognition is a key research area in artificial intelligence, playing a critical role in enhancing human-computer interaction and optimizing user experience design. This study explores the application and effectiveness of ensemble learning methods for emotion recognition based on m...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-06-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-96616-0 |
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| _version_ | 1849469918613864448 |
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| author | Yilong Liao Yuan Gao Fang Wang Li Zhang Zhenrong Xu Yifan Wu |
| author_facet | Yilong Liao Yuan Gao Fang Wang Li Zhang Zhenrong Xu Yifan Wu |
| author_sort | Yilong Liao |
| collection | DOAJ |
| description | Abstract Emotion recognition is a key research area in artificial intelligence, playing a critical role in enhancing human-computer interaction and optimizing user experience design. This study explores the application and effectiveness of ensemble learning methods for emotion recognition based on multiple physiological parameters. A dataset was systematically constructed by preprocessing data from electroencephalogram (EEG), galvanic skin response (GSR), skin temperature (ST), and heart rate (HR) collected from 38 subjects while watching short videos. We proposed a hybrid model framework combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, trained and optimized using a random seed initialization strategy and a cosine annealing warm restart strategy. To further enhance performance, various strategies were designed and evaluated. The results showed that applying advanced preprocessing techniques significantly improved data quality, while the hybrid model effectively leveraged the advantages of both CNN and LSTM. Incorporating the cosine annealing warm restart strategy further boosted model performance. Using a soft voting ensemble method, the proposed approach achieved a 96.21% accuracy rate in classifying seven emotions—calm, happy, disgust, surprise, anger, sad, and fear, indicating its ability to accurately capture emotional responses to short videos. This study presents an innovative approach to emotion recognition using multiple physiological parameters, demonstrating the potential of ensemble learning for complex tasks. It offers valuable insights for the development of effective applications. |
| format | Article |
| id | doaj-art-15e8da5cfd174b388940f5b078aa9d87 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-15e8da5cfd174b388940f5b078aa9d872025-08-20T03:25:18ZengNature PortfolioScientific Reports2045-23222025-06-0115111210.1038/s41598-025-96616-0Emotion recognition with multiple physiological parameters based on ensemble learningYilong Liao0Yuan Gao1Fang Wang2Li Zhang3Zhenrong Xu4Yifan Wu5School of Biomedical Engineering, South-Central Minzu UniversitySchool of Biomedical Engineering, South-Central Minzu UniversitySchool of Biomedical Engineering, South-Central Minzu UniversitySchool of Biomedical Engineering, South-Central Minzu UniversitySchool of Biomedical Engineering, South-Central Minzu UniversitySchool of Biomedical Engineering, South-Central Minzu UniversityAbstract Emotion recognition is a key research area in artificial intelligence, playing a critical role in enhancing human-computer interaction and optimizing user experience design. This study explores the application and effectiveness of ensemble learning methods for emotion recognition based on multiple physiological parameters. A dataset was systematically constructed by preprocessing data from electroencephalogram (EEG), galvanic skin response (GSR), skin temperature (ST), and heart rate (HR) collected from 38 subjects while watching short videos. We proposed a hybrid model framework combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, trained and optimized using a random seed initialization strategy and a cosine annealing warm restart strategy. To further enhance performance, various strategies were designed and evaluated. The results showed that applying advanced preprocessing techniques significantly improved data quality, while the hybrid model effectively leveraged the advantages of both CNN and LSTM. Incorporating the cosine annealing warm restart strategy further boosted model performance. Using a soft voting ensemble method, the proposed approach achieved a 96.21% accuracy rate in classifying seven emotions—calm, happy, disgust, surprise, anger, sad, and fear, indicating its ability to accurately capture emotional responses to short videos. This study presents an innovative approach to emotion recognition using multiple physiological parameters, demonstrating the potential of ensemble learning for complex tasks. It offers valuable insights for the development of effective applications.https://doi.org/10.1038/s41598-025-96616-0Ensemble learningEmotion recognitionCosine annealing warm restartMultiple physiological parametersData processing |
| spellingShingle | Yilong Liao Yuan Gao Fang Wang Li Zhang Zhenrong Xu Yifan Wu Emotion recognition with multiple physiological parameters based on ensemble learning Scientific Reports Ensemble learning Emotion recognition Cosine annealing warm restart Multiple physiological parameters Data processing |
| title | Emotion recognition with multiple physiological parameters based on ensemble learning |
| title_full | Emotion recognition with multiple physiological parameters based on ensemble learning |
| title_fullStr | Emotion recognition with multiple physiological parameters based on ensemble learning |
| title_full_unstemmed | Emotion recognition with multiple physiological parameters based on ensemble learning |
| title_short | Emotion recognition with multiple physiological parameters based on ensemble learning |
| title_sort | emotion recognition with multiple physiological parameters based on ensemble learning |
| topic | Ensemble learning Emotion recognition Cosine annealing warm restart Multiple physiological parameters Data processing |
| url | https://doi.org/10.1038/s41598-025-96616-0 |
| work_keys_str_mv | AT yilongliao emotionrecognitionwithmultiplephysiologicalparametersbasedonensemblelearning AT yuangao emotionrecognitionwithmultiplephysiologicalparametersbasedonensemblelearning AT fangwang emotionrecognitionwithmultiplephysiologicalparametersbasedonensemblelearning AT lizhang emotionrecognitionwithmultiplephysiologicalparametersbasedonensemblelearning AT zhenrongxu emotionrecognitionwithmultiplephysiologicalparametersbasedonensemblelearning AT yifanwu emotionrecognitionwithmultiplephysiologicalparametersbasedonensemblelearning |