The popularity of wearable physiological recording devices has opened up new possibilities for the assessment of personality traits in everyday life. Compared with traditional questionnaires or laboratory assessments, wearable device-based measurements can collect rich data about individual physiological activities in real-life situations without interfering with normal life, enabling a more comprehensive description of individual differences. The present study aimed to explore the assessment of individuals' Big-Five personality traits by physiological signals in daily life situations. A commercial bracelet was used to track the heart rate (HR) data from eighty college students (all male) enrolled in a special training program with a strictly-controlled daily schedule for ten consecutive working days. Their HR activities were divided into five daily situations (morning exercise, morning classes, afternoon classes, free time in the evening, and self-study situations) according to their daily schedule. Regression analyses with HR-based features in these five situations averaged across the ten days revealed significant cross-validated quantitative prediction correlations of 0.32 and 0.26 for the dimensions of Openness and Extraversion, with the prediction correlation trending significance for Conscientiousness and Neuroticism. Moreover, the multi-situation HR-based results were in general superior to those based on single-situation HR-based features, as well as those based on the multi-situation self-reported emotion ratings. Together, our findings demonstrate the link between personality and daily HR measures using state-of-the-art commercial devices and could shed light on the development of Big-Five personality assessment based on daily multi-situation physiological measures.

The objective identification of depression using physiological data has emerged as a significant research focus within the field of psychiatry. The advancement of wearable physiological measurement devices has opened new avenues for the identification of individuals with depression in everyday-life contexts. Compared to other objective measurement methods, wearables offer the potential for continuous, unobtrusive monitoring, which can capture subtle physiological changes indicative of depressive states. The present study leverages multimodal wristband devices to collect data from fifty-eight participants clinically diagnosed with depression during their normal daytime activities over six hours. Data collected include pulse wave, skin conductance, and triaxial acceleration. For comparison, we also utilized data from fifty-eight matched healthy controls from a publicly available dataset, collected using the same devices over equivalent durations. Our aim was to identify depressive individuals through the analysis of multimodal physiological measurements derived from wearable devices in daily life scenarios. We extracted static features such as the mean, variance, skewness, and kurtosis of physiological indicators like heart rate, skin conductance, and acceleration, as well as autoregressive coefficients of these signals reflecting the temporal dynamics. Utilizing a Random Forest algorithm, we distinguished depressive and non-depressive individuals with varying classification accuracies on data aggregated over 6 h, 2 h, 30 min, and 5 min segments, as 90.0%, 84.7%, 80.1%, and 76.0%, respectively. Our results demonstrate the feasibility of using daily wearable-derived physiological data for depression recognition. The achieved classification accuracies suggest that this approach could be integrated into clinical settings for the early detection and monitoring of depressive symptoms. Future work will explore the potential of these methods for personalized interventions and real-time monitoring, offering a promising avenue for enhancing mental health care through the integration of wearable technology.

The view of embodied emotion believes that emotions are the emotions of the body. While emotion-specific patterns of self-reported bodily sensation have been previously reported, the physiological bodily representation across emotions remains to be addressed. The present study aimed to investigate the effectiveness of multi-site bodily electrodermal representations of emotions. A multi-channel electrodermal measurement device was designed to record electrodermal activities from nine body sites (neck, back, chest, bilateral abdomen, bilateral wrist, and bilateral ankle) from thirty-six college students (all male), while they were presented with a series of emotional pictures. Using the integral skin conductance response feature and a random forest classification method, the classification of high and low arousal levels achieved an average classification accuracy of 80.4±8.1%, and the classification of positive, neutral, and negative states reached an average classification accuracy of 76.4±10.2%. The classification models for arousal and valence were found to rely on distinct bodily representations. Meanwhile, the classification results of multi-site measurement were significantly better than single-site results. Our findings for the first time illustrate the bodily electrodermal representations of emotion and suggest the feasibility of affective computing using bodily electrodermal signals.

Emotion recognition by wearable devices is essential for advancing emotion-aware human–computer interaction in real life. Earphones have the potential to naturally capture brain activity and its lateralization, which is associated with emotion. In this study, we newly introduced tympanic membrane temperature (TMT), previously used as an index of lateralized brain activation, for earphone-based emotion recognition. We developed custom earphones to measure bilateral TMT and conducted two experiments consisting of emotion induction by autobiographical recall and scenario imagination. Using features derived from the right–left TMT difference, we trained classifiers for both four-class discrete emotion and valence (positive vs. negative) classification tasks. The classifiers achieved 36.2% and 42.5% accuracy for four-class classification and 72.5% and 68.8% accuracy for binary classification, respectively, in the two experiments, confirmed by leave-one-participant-out cross-validation. Notably, consistent improvement in accuracy was specific to models utilizing right–left TMT and not observed in models utilizing the right–left wrist skin temperature. These findings suggest that lateralization in TMT provides unique information about emotional state, making it valuable for emotion recognition. With the ease of measurement by earphones, TMT has significant potential for real-world application of emotion recognition.