Li Jiayu, Ma Weizhi, Zhang Min, Wang Pengyu, Liu Yiqun, Ma Shaoping
Department of Computer Science and Technology, Institute for Artificial Intelligence, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China.
Front Digit Health. 2021 Aug 11;3:676824. doi: 10.3389/fdgth.2021.676824. eCollection 2021.
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, , for multi-dimensional self-awareness lifelog data collection.
自我意识是生理学和心理学中的一个重要概念。准确的整体自我意识有利于个体的发展和幸福。以往关于自我意识的研究主要通过问卷调查、用户研究或实地研究在实验室环境中收集和分析数据。然而，这些方法通常不是实时的，不适用于日常生活应用。因此，我们提出了一种利用生活日志进行自我意识研究的新方向。关于日常活动的生活日志记录被用于对个体的身体和心理状态进行分析、预测和干预，这些记录可以实时自动处理。借助生活日志，普通人能够更精确地了解自己的状况，获得有效的个人健康建议，甚至在早期发现身心异常。作为将生活日志用于自我意识研究的第一步，我们借鉴传统机器学习问题，总结了生活日志场景下的数据收集、特征提取、标签标注和模型学习的模式。该模式为生活日志应用提供了一种灵活且保护隐私的方法。按照该模式，我们开展了四个主题的研究：睡眠质量预测、性格检测、情绪检测与预测以及抑郁检测。对真实数据集的实验在这些主题上取得了令人鼓舞的结果，揭示了日常活动记录与身体和心理自我意识之间的显著关系。最后，我们详细讨论了实验结果和局限性，并提出了一个用于多维自我意识生活日志数据收集的应用程序。
