ABSTRACT
Emotion reacts human beings' physiological and psychological status. Galvanic Skin Response (GSR) can reveal the electrical characteristics of human skin and is widely used to recognize the presence of emotion. In this work, we propose an emotion recognition frame-work based on deep hybrid neural networks, in which 1D CNN and Residual Bidirectional GRU are employed for time series data analysis. The experimental results show that the proposed method can outperform other state-of-the-art methods. In addition, we port the proposed emotion recognition model on Raspberry Pi and design a real-time emotion interaction robot to verify the efficiency of this work.
- Atif Alamri. 2018. Monitoring system for patients using multimedia for smart healthcare. IEEE Access6 (2018), 23271--23276.Google ScholarCross Ref
- Anna Aljanaki, Yi-Hsuan Yang, and Mohammad Soleymani. 2017. Developing a benchmark for emotional analysis of music. PloS one 12, 3 (2017), e0173392.Google ScholarCross Ref
- Sarah E Garcia and Laura M Hammond. 2016. Capturing & Measuring Emotions in UX. In Proceedings of the ACM CHI Conference Extended Abstracts on Human Factors in Computing Systems. 777--785.Google ScholarDigital Library
- A. Greco, G. Valenza, A. Lanata, E. P. Scilingo, and L. Citi. 2016. cvxEDA: A Convex Optimization Approach to Electrodermal Activity Processing. IEEE Transactions on Biomedical Engineering63, 4 (2016), 797--804.Google Scholar
- Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE conference on Computer Vision and Pattern Recognition. 770--778.Google ScholarCross Ref
- Wei Jiang, Zheng Wang, Jesse S Jin, Xianfeng Han, and Chunguang Li. 2019. Speech Emotion Recognition with Heterogeneous Feature Unification of Deep Neural Network. Sensors19, 12 (2019), 2730.Google Scholar
- Gil Keren, Tobias Kirschstein, Erik Marchi, Fabien Ringeval, and Björn Schuller. 2017. End-to-end learning for dimensional emotion recognition from physiological signals. In Proceedings of IEEE International Conference on Multimedia and Expo. 985--990.Google ScholarCross Ref
- Ruhul Amin Khalil, Edward Jones, Mohammad Inayatullah Babar, Tariqullah Jan, Mohammad Haseeb Zafar, and Thamer Alhussain. 2019. Speech emotion recognition using deep learning techniques: A review. IEEE Access 7 (2019), 117327--117345.Google ScholarCross Ref
- Diederik P. Kingma and Jimmy Ba. 2015. Adam: A Method for Stochastic Optimization. In Proceedings of International Conference on Learning Representations.Google Scholar
- Felix Klotzsche, Alberto Mariola, Simon Hofmann, Vadim V Nikulin, Arno Villringer, and Michael Gaebler. 2018. Using EEG to decode subjective levels of emotional arousal during an immersive VR roller coaster ride. In Proceedings of IEEE Conference on Virtual Reality and 3D User Interfaces (VR).Google ScholarCross Ref
- Takurou Magaki and Michael Vallance. 2019. Developing an Accessible Evaluation Method of VR Cybersickness. In Proceedings of IEEE Conference on Virtual Reality and 3D User Interfaces (VR).Google ScholarCross Ref
- Yoko Nagai, Christopher Iain Jones, and Arjune Sen. 2019. Galvanic skin response (GSR)/electrodermal/skin conductance biofeedback on epilepsy: a systematic review and meta-analysis. Frontiers in neurology10 (2019), 377.Google Scholar
- Najmeh Samadiani, Guangyan Huang, Borui Cai, Wei Luo, Chi-Hung Chi, Yong Xiang, and Jing He. 2019. A review on automatic facial expression recognition systems assisted by multimodal sensor data. Sensors 19, 8 (2019), 1863.Google ScholarCross Ref
- M. Schuster and K. K. Paliwal. 1997. Bidirectional recurrent neural networks. IEEE Transactions on Signal Processing 45, 11 (1997), 2673--2681.Google ScholarDigital Library
- Lin Shu, Jinyan Xie, Mingyue Yang, Ziyi Li, Zhenqi Li, Dan Liao, Xiangmin Xu, and Xinyi Yang. 2018. A review of emotion recognition using physiological signals. Sensors18, 7 (2018), 2074.Google Scholar
- J. Shukla, M. Barreda-Angeles, J. Oliver, G. C. Nandi, and D. Puig. 2019. Feature Extraction and Selection for Emotion Recognition from Electrodermal Activity. IEEE Transactions on Affective Computing(2019), 1--1.Google Scholar
- Goran Udovicic, Jurica Ðerek, Mladen Russo, and Marjan Sikora. 2017. Wearable Emotion Recognition System Based on GSR and PPG Signals. In Proceedings of the 2Nd International Workshop on Multimedia for Personal Health and Health Care (MMHealth '17). 53--59.Google ScholarDigital Library
- Wei Wei, Qingxuan Jia, Feng Yongli, and Gang Chen. 2018. Emotion Recognition Based on Weighted Fusion Strategy of Multichannel Physiological Signals. Computational Intelligence and Neuroscience 2018 (07 2018), 1--9.Google Scholar
- G. Yin, S. Sun, H. Zhang, D. Yu, C. Li, K. Zhang, and N. Zou. 2019. User Independent Emotion Recognition with Residual Signal-Image Network. In Proceedings of IEEE International Conference on Image Processing (ICIP). 3277--3281.Google Scholar
- Zhong Yin, Yongxiong Wang, Li Liu, Wei Zhang, and Jianhua Zhang. 2017. Cross-Subject EEG Feature Selection for Emotion Recognition Using Transfer Recursive Feature Elimination. In Front. Neurorobot.Google Scholar
- Kejun Zhang, Hui Bin Zhang, Simeng Li, Chang yuan Yang, and Lingyun Sun. 2018. The PMEmo Dataset for Music Emotion Recognition. In Proceedings of the ACM International Conference on Multimedia Retrieval. 135--142.Google ScholarDigital Library
Index Terms
- Emotion Recognition from Galvanic Skin Response Signal Based on Deep Hybrid Neural Networks
Recommendations
Detecting Users’ Cognitive Load by Galvanic Skin Response with Affective Interference
Experiencing high cognitive load during complex and demanding tasks results in performance reduction, stress, and errors. However, these could be prevented by a system capable of constantly monitoring users’ cognitive load fluctuations and adjusting its ...
Are paired or single stimuli better to recognize genuine and posed smiles from observers’ galvanic skin response?
OzCHI '20: Proceedings of the 32nd Australian Conference on Human-Computer InteractionSmile recognition plays a vital role in human-human and human-computer interactions. This paper demonstrates a system to recognize the genuine and posed smiles by sensing observers’ galvanic skin response (GSR), while watching sets of images and ...
Group emotion recognition in the wild by combining deep neural networks for facial expression classification and scene-context analysis
ICMI '17: Proceedings of the 19th ACM International Conference on Multimodal InteractionThis paper presents the implementation details of a proposed solution to the Emotion Recognition in the Wild 2017 Challenge, in the category of group-level emotion recognition. The objective of this sub-challenge is to classify a group's emotion as ...
Comments