# Review of Studies on Emotion Recognition and Judgment Based on Physiological Signals
## 5 Ethical and Privacy Concerns Related to the Use of Physiological Signals for Emotion Recognition  
Ethical and privacy issues should be given attention in emotion recognition and judgment because they relate to relevant data extracted from people along with their personal and behavioral information [96]. The former may reflect physical and psychological health, whereas the latter may include health, location, and other highly confidential information [97]. In addition, emotion research based on physiological signals could reveal the internal characteristics of individuals [98]. Therefore, ethical issues including the protection of user privacy deserve more attention. [PERSON] and [PERSON] [99] indicated that such private information could be exploited, causing violations of personal rights, so ethical approval is imperative before data acquisition. Namely ethical consent, which includes general, personal and researcher concerns, must be obtained from the participant. They also detailed the specific contents of the three levels of concern. In daily life and work, there is a potential privacy problem with the leakage of physiological information, including physiological signals and facial expressions. [PERSON] and [PERSON] [100] presented a new approach based on a pre-trained 3D convolutional neural network, i.e., Privacy-Phys, to modify remote photoplethysmography in facial videos for privacy. The results showed that the approach was more effective and efficient than the previous baseline. [PERSON] et al. [101] pointed out that digital identity and identity management must be taken into consideration because of the dynamic nature and scale of the number of devices, applications, and associated services in a large-scale emotion recognition system. [PERSON] et al. [102] presented a new affective virtual reality system by adopting the immersion, privacy and design flexibility of virtual reality based on EEG, EDA and heart rate signals. The results proved that virtual reality scenes could achieve the same emotion elicitation as video. [PERSON] et al. [103] developed a complete product to use the detected emotions in a real-time application based on ECG, EMG, respiration and skin temperature data. The signals were obtained with personal wearable technology while not compromising privacy. Accuracy of 91.81% was obtained in an experiment with 150 participants. Monitoring of the elderly using visual and physiological devices is likely to breach privacy. Reading distress emotions with speech would provide effective monitoring while preserving the privacy of the elderly. [PERSON] et al. [104] presented a new method where association rules drawn from speech features were used to derive the correlation between features and feed these correlations to machine learning techniques for distress detection. [PERSON] et al. [105] presented a privacy-preserving representation-learning variational generative adversarial network to learn an image representation that was explicitly disentangled from the identity information, and the test results showed quantitatively and qualitatively that the presented approach could reach a balance between the preservation of privacy and data utility. [PERSON] et al. [106] presented an image melding-based method that altered facial regions in a visually unintrusive way while preserving facial expression, and the test results indicated that the presented approach could retain facial expression while protecting privacy. [PERSON] et al. [107] presented an effective and robust solution for facial expression recognition under an unconstrained environment that could classify facial images in the client/server model while preserving privacy, and by validating the results on four popular and versatile databases, they elaborated that the effectiveness of the presented approach remained unchanged by preserving privacy.