Know Yourself: Physical and Psychological Self-Awareness With Lifelog

Self-awareness is an essential concept in physiology and psychology. Accurate overall self-awareness benefits one’s development and well-being. Previous researches on self-awarenessmainly collect and analyze data in the lab environment through questionnaires, user study, orfield research. However, these methods are usually not real-time and unavailable for daily lifeapplications. Therefore, we propose a new direction of utilizing lifelog for self-awareness. Lifelogrecords about daily activities are used for analysis, prediction, and intervention on individualphysical and psychological status, which can be automatically processed in real-time. Withthe help of lifelog, ordinary people are able to understand their condition more precisely, geteffective personal advice about health, and even discover physical and mental abnormality atan early stage. As the first step on using lifelog for self-awareness, we learn from the traditionalmachine learning problems, and summarize a schema on data collection, feature extraction,label tagging, and model learning in the lifelog scenario. The schema provides a flexible andprivacy-protected method for lifelog applications. Following the schema, four topics are conducted:sleep quality prediction, personality detection, mood detection and prediction, and depressiondetection. Experiments on real datasets show encouraging results on these topics, revealing thesignificant relation between daily activity records and physical and psychological self-awareness.In the end, we discuss the experiment results and limitations in detail, and propose an application,Lifelog Recorder, for multi-dimensional self-awareness lifelog data collection.