Daily Mood Assessment Based on Mobile Phone Sensing

With the increasing stress and unhealthy lifestyles in people´s daily life, mental health problems are becoming a global concern. In particular, mood related mental health problems, such as mood disorders, depressions, and elation, are seriously impacting people´s quality of life. However, due to the complexity and unstableness of personal mood, assessing and analyzing daily mood is both difficult and inconvenient, which is a major challenge in mental health care. In this paper, we propose a novel framework called Mood Miner for assessing and analyzing mood in daily life. Mood Miner uses mobile phone data - mobile phone sensor data and communication data (including acceleration, light, ambient sound, location, call log, etc.) - to extract human behavior pattern and assess daily mood. Our approach overcomes the problem of subjectivity and inconsistency of traditional mood assessment methods, and achieves a fairly good accuracy (around 50%) with minimal user intervention. We have built a system with clients on Android platform and an assessment model based on factor graph. We have also carried out experiments to evaluate our design in effectiveness and efficiency.