About this Research Topic
The emerging field of affective computing including transdisciplinary research areas such as psycho-physiology, computer science, and biomedical engineering has gained intense attention over the last few decades. Within this field, recognizing the emotional states and understanding emotion regulation processes are of primary objectives for improving mental healthcare.
The affective states are generally reflected in facial expressions and body gestures, behavioral information, and physiological signals (e.g. electrocardiography, electrodermal activity, electroencephalography, respiration, blood pressure, and electromyography). Advances in digital mental healthcare systems and the design of interactive-based ecological assessment platforms have facilitated the capture and modeling of the affect information for the purpose of supporting the emotional wellbeing of effective health. Research in this field concerns not only the tracking of emotional states but also the awareness of emotions and people’s ability to adaptively self-regulate their emotional responses.
Despite the advances in our scientific understanding of emotion and the emotion regulation process, there are still challenges to be addressed due to the complex mechanisms underlying human emotions. The scientific community has not reached a consensus regarding the cognitive, psychological, and physiological mechanisms mediating the emotions, which raises the question of constructing valid models for emotional processing. Moreover, most of the existing data is captured in controlled lab conditions which makes their robustness in real-world scenarios questionable. Therefore, there is a need to develop accurate and inexpensive sensing devices that are not only acceptable by the users but also provide high-quality data for better recognition of an individual’s emotional states and better support for the self-regulation of such states. The key is novel signal processing and machine learning algorithms that could aid the offline and online processing of the data from biosensing devices to both to understand emotional states and support emotional regulation through smart actuators.
This Research Topic intends to collect recent findings in the affective computing realm to address the current challenges in affective health in the form of Original Research, Perspective, and Review articles. This article collection will cover (but is not limited to) the following areas of research:
· Proposing novel signal processing algorithms for analyzing physiological signals to understand emotion and emotion regulation processing in humans
· Advancing feature dependent machine learning (ML) models and feature independent ML methodologies (Deep learning) to identify emotional states
· Developing mobile and wearable technologies for effective health
· Modeling and quantifying the psychophysiological phenomena underlying the human emotional states
· Integrating insights on emotion regulation process and emotional self-awareness from social psychology, developmental psychology, neuropsychology, health psychology, and clinical psychology
· Designing neuro-feedback and biofeedback platforms
Dr. Shadi Ghiasi stands as the Co-ordinator for this Research Topic. We would like to acknowledge her contribution to the preparation of this Topic and her involvement in the collection.
Dr. Alberto Greco is a co-founder and the CEO of Feel-ING s.r.l. All other Topic Editors declare no competing interests with regard to the Research Topic subject.
Keywords: Emotion, Emotion regulation, healthcare, sensors, technology
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.