A Deep Residual-based Model on Multi-Branch Aggregation for Stress and Emotion Recognition through Biosignals
Stress and emotion recognition (SER) is a rapidly growing field of study that has applications in various areas, including psychological wellbeing, rehabilitative services, athletic training, and human-computer interaction. Biological information such as the electrocardiogram (ECG), electromyography...
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| Main Authors: | , , |
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| 其他作者: | |
| 格式: | Conference or Workshop Item |
| 出版: |
2022
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| 主題: | |
| 在線閱讀: | https://repository.li.mahidol.ac.th/handle/123456789/73756 |
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| 機構: | Mahidol University |
| 總結: | Stress and emotion recognition (SER) is a rapidly growing field of study that has applications in various areas, including psychological wellbeing, rehabilitative services, athletic training, and human-computer interaction. Biological information such as the electrocardiogram (ECG), electromyography (EMG), and electrodermal activity (EDA) has been frequently utilized for the SER for learning-based approaches. This study introduces a convolutional neural network motivated by ResNeXt to facilitate multimodal awareness. The proposed model, named StressNeXt, can extract high-level insights from raw bio-signal signals and classify emotional expressions effectively. We undertake a series of investigations using a publicly released standard dataset (WESAD) to determine the optimal implementation of the proposed solution for recognizing stress and emotion. After incorporating preliminary fusion events, we examined deep learning models using 5-fold cross-validation. Our study demonstrates that the suggested technique can comprehend robust multimodal representations with an accuracy of 87.73% utilizing EDA. Additionally, the identification was designed to provide better to 99.92% by fusing with accelerometer sensor data. |
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