Personal and ubiquitous sensing technologies such as smartphones have allowed the
continuous collection of data in an unobtrusive manner. Machine learning methods have …

Background Electronic mental health interventions for mood disorders have increased
rapidly over the past decade, most recently in the form of various systems and apps that are …

Wearable health sensors could monitor the wearer's health and surrounding environment in
real-time. With the development of sensor and operating system hardware technology, the …

Affective Computing has emerged as an important field of study that aims to develop
systems that can automatically recognize emotions. Up to the present, elicitation has been …

Emotion recognition through computational modeling and analysis of physiological signals
has been widely investigated in the last decade. Most of the proposed emotion recognition …

This paper reports on how emotional states elicited by affective sounds can be effectively
recognized by means of estimates of Autonomic Nervous System (ANS) dynamics …

Medical activity requires responsibility not only based on knowledge and clinical skills, but
also in managing a vast amount of information related to patient care. It is through the …

Monitoring and modeling biomedical, health care and wellness data from individuals and
converging data on a population scale have tremendous potential to improve understanding …

Abstract Machine learning techniques provide new methods to predict diagnosis and clinical
outcomes at an individual level. We aim to review the existing literature on the use of …

The increasing use of electronic forms of communication presents new opportunities in the
study of mental health, including the ability to investigate the manifestations of psychiatric …