Abstract
With the advancement of human-computer interaction and artificial intelligence, emotion recognition has received significant research attention. The most commonly used technique for emotion recognition is EEG, which is directly associated with the central nervous system and contains strong emotional features. However, there are some disadvantages to using EEG signals. They require high dimensionality, diverse and complex processing procedures which make real-time computation difficult. In addition, there are problems in data acquisition and interpretation due to body movement or reduced concentration of the experimenter. In this paper, we used photoplethysmography (PPG) and electromyography (EMG) to record signals. Firstly, we segmented the emotion data into 10-pulses during preprocessing to identify emotions with short period signals. These segmented data were input to the proposed bimodal stacked sparse auto-encoder model. To enhance recognition performance, we adopted a bimodal structure to extract shared PPG and EMG representations. This approach provided more detailed arousal-valence mapping compared with the current high/low binary classification. We created a dataset of PPG and EMG signals, called the emotion dataset dividing into four classes to help understand emotion levels. We achieved high performance of 80.18% and 75.86% for arousal and valence, respectively, despite more class classification. Experimental results validated that the proposed method significantly enhanced emotion recognition performance.
Highlights
HCI as well as human-to-human interaction have become very important as artificial advance and has expanded to various fields, including using emotion in computers, which has recently attracted significant research attention
Emotion recognition methods based on physiological signals such as photoplethysmography (PPG), electromyography (EMG), electroencephalography (EEG), electrooculography, electro-skin response, and respiration are more objective and reliable [5,6,7,8]
Bimodal deep learning can learn high-level shared representations between two modalities, whereas the proposed BSSAE structure uses explicitly shared and automatically extracted representations
Summary
HCI as well as human-to-human interaction have become very important as artificial advance and has expanded to various fields, including using emotion in computers, which has recently attracted significant research attention. Emotion research is a promising area for future development. Emotions are complex psycho-physiological processes associated with external and internal activities [2,3]. They can be consciously recognized from facial expressions, speech, text, gestures [4], these are not reliable indicators since they can be falsified by individuals or may not be produced as a result of the implied emotion. Physiological signals are more accurate indicators because they generally cannot be consciously controlled. Emotion recognition methods based on physiological signals such as photoplethysmography (PPG), electromyography (EMG), electroencephalography (EEG), electrooculography, electro-skin response, and respiration are more objective and reliable [5,6,7,8]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
