Know Yourself: Physical and Psychological Self-Awareness With Lifelog
Self-awareness is an essential concept in physiology and psychology. Accurate overall self-awareness benefits the development and well being of an individual. The previous research studies on self-awareness mainly collect and analyze data in the laboratory environment through questionnaires, user study, or field research study. However, these methods are usually not real-time and unavailable for daily life applications. Therefore, we propose a new direction of utilizing lifelog for self-awareness. Lifelog records about daily activities are used for analysis, prediction, and intervention on individual physical and psychological status, which can be automatically processed in real-time. With the help of lifelog, ordinary people are able to understand their condition more precisely, get effective personal advice about health, and even discover physical and mental abnormalities at an early stage. As the first step on using lifelog for self-awareness, we learn from the traditional machine 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 and privacy-protected method for lifelog applications. Following the schema, four topics were conducted: sleep quality prediction, personality detection, mood detection and prediction, and depression detection. Experiments on real datasets show encouraging results on these topics, revealing the significant 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.
1. Introduction
As an important concept in biological and psychological studies, self-awareness is the experience of own personality or individuality of an individual (1). It describes how an individual consciously understands their character, feelings, and desires. The previous studies on self-awareness mainly explored the physical and mental status of people with human effort, such as field research study (2), user study (3), and questionnaires (4). However, these methods are time-consuming and limited to the laboratory environment, which is impossible for large-scale applications in daily life.
With the development of portable devices and storage techniques, recording daily life with digital sensors, such as wristbands and smartphones, becomes increasingly popular. As a result, lifelog, as the data reflection on life experience passively gathered and processed with multimedia sensors (5), has drawn attention in academy and industry. The lifelog provides a new possibility for self-awareness detection, as it has been proved that daily activity reflects the subjective health status of people (6).
Current lifelog analysis mainly focuses on the objective reflection of lifelog for a specific short-term goal, such as scene searching, diet monitoring, and item recommendation. However, the ultimate goal of lifelog should be to record detailed memories in the daily activities of an individual and help people better understand and live their life (5). Therefore, it is necessary to move from objective analysis to subjective understanding of lifelog, and connect lifelog with the lifelogger more tightly.
Having noticed this tendency in lifelogging research studies, we propose to improve the life-long self-awareness of people on the physical and mental status with the help of lifelog records. We integrate a series of our recent research studies about applying lifelogging to detect subjective self-awareness, revealing the great potential of lifelog on self-exploring for ordinary people.
To be specific, four topics about life-long self-awareness exploration based on lifelog data are introduced, from physical analysis on sleep quality prediction, to psychological exploration about static personality detection, real-time mood detection and prediction, and further depression mood detection. Generally, experiments on all topics follow the same schema: 1) lifelog data collection, 2) activity features extraction, 3) label tagging, and 4) prediction/detection with popular machine learning models. All four topics give insight into the relation between lifelog and the physical/psychological status of lifeloggers, and the experiments show that lifelog does reflect abundant information for self-understanding. However, due to the limitation of dataset collection, cross-task analyses are not applied in these experiments. To formalize lifelog data collection and achieve the goal of long-term stable records for ordinary people, we build an application for semi automatic multi-dimensional dataset collection, which frees users from integrating heterogeneous data, helpful for the more in-depth research studies on lifelog and psychology in the future.
The main contributions of this study are as follows: 1) We reveal a new inter-disciplinary direction for lifelog and psychology. A relation from objective data to subjective self-awareness is established. Moreover, four new topics were proposed about sleep, personality, and mood. To the best of our knowledge, this is the first study to conduct these subjective understanding tasks via lifelog records. Traditionally, psychological experts assist people in detecting their status. With lifelog, ordinary people can make daily detection by themselves, and early awareness of physical and mental diseases (e.g., depression) is possible. 2) We summarize a schema for lifelog-based analysis, prediction, and intervention, providing a flexible and general framework for practical applications on multiple scenarios. Different from traditional machine learning problems, lifelog data are usually highly heterogeneous and personalized, based on a self-collected dataset. So the data collection, feature processing, and label tagging occupy a large chunk in the experiments. 3) Experiments on real data show encouraging results on multiple self-awareness tasks, demonstrating the capacity of lifelog for automatic perception on self-awareness. Moreover, we provide a novel light-weight method and application for collecting lifelog data. 4) Active or passive data by simple portable devices are supported in experiments. Meanwhile, the applied models are light and possible to deploy on personal devices such as smartphones. This means the data collection and analysis can be dealt with locally, which helps protect privacy for users.
In the rest of this study, we will review the related work in section 2, and give an overview of the schema and topics in section 3. Then, how we adopt the four topics about lifelog-based self-awareness are introduced, respectively
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