M.S.H. Aung

TL;DR: This paper introduces MyBehavior, a smartphone application that takes a novel approach to generate deeply personalized health feedback that combines state-of-the-art behavior tracking with algorithms that are used in recommendation systems.

TL;DR: Initial results from an ongoing randomized control trial, where passive smartphone sensor data is collected from 21 outpatients with schizophrenia recently discharged from hospital over a period ranging from 2-8.5 months, indicate that there are statistically significant associations between automatically tracked behavioral features related to sleep, mobility, conversations, smart-phone usage and self-reported indicators of mental health in schizophrenia.

TL;DR: A review of recent studies focused on measuring human behavior using smartphones and their embedded mobile sensors is provided in this article, which describes the daily behaviors captured using these methods, which include movement behaviors (physical activity, mobility patterns), social behaviors (face-to-face encounters, computer-mediated communications), and other daily activities (non-mediated and mediated activities).

TL;DR: The factors and challenges in the automated recognition of such expressions and behaviour are described and potential avenues for development of such systems are discussed by discussing potential avenues in the context of these findings.

TL;DR: Advancements in mobile technology are enabling collection of an abundance of information that until recently was largely inaccessible to clinical research and practice, however, remote monitoring and relapse detection is in its nascence.