Wearable Technology in Clinical Practice for Depressive Disorder | New England Journal of
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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 …

We exploit a self-supervised deep multi-task learning framework for electrocardiogram
(ECG)-based emotion recognition. The proposed solution consists of two stages of learning …

Robots have the potential to facilitate future therapies for children on the autism spectrum.
However, existing robots are limited in their ability to automatically perceive and respond to …

Recent research has explored computational tools to manage workplace stress via personal
sensing, a measurement paradigm in which behavioral data streams are collected from …

Mobile sensing data processed using machine learning models can passively and remotely
assess mental health symptoms from the context of patients' lives. Prior work has trained …

Bringing emotion recognition (ER) out of the controlled laboratory setup into everyday life
can enable applications targeted at a broader population, eg, helping people with …

The integration of machine/deep learning and sensing technologies is transforming
healthcare and medical practice. However, inherent limitations in healthcare data, namely …

Background The assessment of behaviors related to mental health typically relies on self-
report data. Networked sensors embedded in smartphones can measure some behaviors …

With the rapid development of mobile and wearable devices, it is increasingly possible to
access users' affective data in a more unobtrusive manner. On this basis, researchers have …