Key points are not available for this paper at this time.
This paper explores the utilization of physiological signals, including heart rate variability (HRV) and electroencephalography (EEG), in emotion recognition through wearable devices. Heart Rate Variability (HRV) is closely linked to emotional arousal. HRV can detect subtle changes in heart rate patterns, which are indicative of different emotional states. By analyzing these patterns, researchers can identify and differentiate between various emotions someone may be experiencing. The integration of heart sound signals alongside traditional ECG signals presents an innovative approach, enhancing the accuracy of emotion recognition systems. Similarly, EEG rhythms are investigated for their association with cognitive and emotional states. This involves using brain rhythm sequences in classification tasks. The study underscores the significance of single-channel selection in EEG-based emotion recognition, demonstrating notable improvements in accuracy. Furthermore, wearable emotion recognition devices offer potential benefits for personalized emotion management and mental health intervention, catering to individuals with affective disorders and aiding in medical diagnosis and treatment. The Smartex S.R.L. platform exemplifies the advancement in wearable monitoring technology, facilitating data acquisition and interpretation for emotion recognition, particularly in patients with bipolar disorder. Overall, the paper highlights the evolving landscape of emotion recognition technology, with HRV and EEG emerging as prominent techniques alongside advancements in wearable device design and signal processing methodologies.
Jiahao Lin (Mon,) studied this question.