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The investigation and the classification of the physiological signals involved in fear perception is complicated due to the difficulties in reliably eliciting and measuring the complex construct of fear. Especially, using Virtual Reality (VR) games can well elicit the physiological responses, then it can be used developing treatments in healthcare domain. In this study, we carried out exploratory physiological data analysis and wearable sensory device feasibility for the responses of fear. We contributed 1) to use a of-the-shelf commercial game (Half Life-Alyx) to provoke fear emotion, 2) to demonstrate a performance analysis with different deep learning models like Convolutional Neural Network (CNN), Long-Short Term Memory (LSTM) and Transformer, 3) to investigate the most responsive physiological signal by comprehensive data analysis and best sensory device in terms of multi-level of fear classification. Accuracy metrics, f1-scores and confusion matrices showed that ECG and ACC are the most significant two signals for fear recognition.
Yalcin et al. (Sat,) studied this question.