In Sync : the effect of physiology feedback on the match between heart rate and self-reported stress

Over the past years self-tracking of physiological parameters has become increasingly common: more and more people are keeping track of aspects of their physiological state (e.g., heart rate, blood sugar, and blood pressure). To shed light on the possible effects of self-tracking of physiology, a study was conducted to test whether physiology feedback has acute effects on self-reported stress and the extent to which self-reported stress corresponds to physiological stress. In this study, participants executed several short tasks, while they were either shown visual feedback about their heart rate or not. Results show that self-reported stress is more in sync with heart rate for participants who received physiology feedback. Interactions between two personality factors (neuroticism and anxiety sensitivity) and feedback on the level of self-reported stress were found, indicating that while physiology feedback may be beneficial for individuals high in neuroticism, it may be detrimental for those high in anxiety sensitivity. Additional work is needed to establish how the results of this study may extend beyond immediate effects in a controlled lab setting, but our results do provide a first indication of how self-tracking of physiology may lead to better body awareness and how personality characteristics can help us predict which individuals are most likely to benefit from self-tracking of physiology

Affective Man-Machine Interface: Unveiling human emotions through biosignals

As is known for centuries, humans exhibit an electrical profile. This profile is altered through various psychological and physiological processes, which can be measured through biosignals; e.g., electromyography (EMG) and electrodermal activity (EDA). These biosignals can reveal our emotions and, as such, can serve as an advanced man-machine interface (MMI) for empathic consumer products. However, such a MMI requires the correct classification of biosignals to emotion classes. This chapter starts with an introduction on biosignals for emotion detection. Next, a state-of-the-art review is presented on automatic emotion classification. Moreover, guidelines are presented for affective MMI. Subsequently, a research is presented that explores the use of EDA and three facial EMG signals to determine neutral, positive, negative, and mixed emotions, using recordings of 21 people. A range of techniques is tested, which resulted in a generic framework for automated emotion classification with up to 61.31% correct classification of the four emotion classes, without the need of personal profiles. Among various other directives for future research, the results emphasize the need for parallel processing of multiple biosignals