Modern smartwatches or wrist wearables having multiple physiological sensing modalities
have emerged as a subtle way to detect different mental health conditions, such as anxiety …

There has been a growing focus on the use of artificial intelligence and machine learning for
affective computing to further enhance user experience through emotion recognition …

To further extend the applicability of wearable sensors in various domains such as mobile
health systems and the automotive industry, new methods for accurately extracting subtle …

Affect recognition aims to detect a person's affective state based on observables, with the
goal to eg provide reasoning for decision making or support mental wellbeing. Recently …

Affect recognition is an interdisciplinary research field bringing together researchers from
natural and social sciences. Affect recognition research aims to detect the affective state of a …

Abstract Multi-Task Learning (MTL) is a framework, where multiple related tasks are learned
jointly and benefit from a shared representation space, or parameter transfer. To provide …

Wearable sensors (smart watches, health/fitness trackers, etc.) are experiencing an
explosion in popularity. Their pervasiveness allows for effective data collections to quantify …

Background: There are a wide range of potential adverse health effects, ranging from
headaches to cardiovascular disease, associated with long-term negative emotions and …

Utilizing sensors for affect detection in adaptive learning technologies has been the subject
of growing interest in recent years. This extends to the collection of multiple concurrent …

Extracting emotions from physiological signals has become popular over the past decade.
Recent advancements in wearable smart devices have enabled capturing physiological …