This study provides evidence that personality can be reliably predicted from activity data
collected through mobile phone sensors. Employing a set of well informed indicators …

The nine articles in this special section focus on the use of deep learning program for mobile
sensing technologies and applications. Smart devices equipped with a rich set of sensors …

Automatic recognition of user context is essential for a variety of emerging applications, such
as context-dependent content delivery, telemonitoring of medical patients, or quantified life …

To tap into the gold mine of data generated by Internet of Things (IoT) devices with
unprecedented volume and value, there is an urgent need to efficiently and accurately label …

Mental stress is a critical factor affecting one's physical and mental well-being. At the early
stage, the effect of stress is often underestimated, while it usually leads to serious issue …

Social interactions have multifaceted effects on individuals' mental health statuses, including
mood and stress. As a proxy for the social environment, Bluetooth encounters detected by …

Recently, inferring the state of people's mental health via passive mobile sensing has
attracted significant attention. Previous studies have used the self-assessed stress levels as …

Current research in passive health monitoring utilizes machine learning methods to infer
users' symptoms and health status from smartphone-sensed data, which can be gathered on …

Mental illness is becoming a major plague in modern societies and poses challenges to the
capacity of current public health systems worldwide. With the widespread adoption of social …

The ubiquity of mobile phones and the increasing availability of wearable sensors have
sparked the interest of the research community to use mobile devices as a platform for …