Smartphone health sensing tools, which analyze passively gathered human behavior data,
can provide clinicians with a longitudinal view of their patients' ailments in natural settings. In …

Abstract Human Bio-Behavioral Rhythms (HBRs) such as sleep-wake cycles (Circadian
Rhythms), and the degree of regularity of sleep and physical activity have important health …

Human context recognition (HCR) using sensor data is a crucial task in Context-Aware (CA)
applications in domains such as healthcare and security. Supervised machine learning HCR …

Digital phenotyping is the characterization of human behavior patterns based on data from
digital devices such as smartphones in order to gain insights into the users' state and …

There has recently been increased interest in context-aware mobile sensing applications
due to the ubiquity of sensor-rich smartphones. Our DARPA-funded Warfighter Analytics for …

In big data applications from digital health to assisted living smart systems, only a fraction of
data instances used for training classifiers t end to be labeled. One important subfield of …

The development of mobile health (mHealth) assessment machine learning models requires
data gathering studies in which smartphone sensor data is gathered continuously from …

Human Context Recognition (HCR) from smart-phone sensor data is an essential task in
Context-Aware (CA) systems including those targeting healthcare and security. Two types of …

The works of ancient Chinese literature that rely on Internet technologies are developing
quickly. Through mobile phone inspection, ancient Chinese literary masterpieces are …

Deep learning is revolutionizing all areas of data science, including human factors research.
Much of human factors data, however, have fundamental idiosyncrasies that make applying …