Mining Based Time-Series Sleeping Pattern Analysis for Life Big-Data

Humans require rest in order to recover from physical and psychological fatigue caused by daily routines, and either medication or sleep is needed to achieve this. If there is a lack of sleep, many health issues can occur, such as depression, anxiety, obsession, dementia, deteriorated immunity, and lowered physical ability. In modern society, a variety of sleep disorders including insomnia, hypnolepsy, sleep apnea, and anxietas tibiarum, occur depending on the environment or occupations. As such, sleep significantly influences the quality of life, and it is a fundamental factor of healthcare. Thus, to increase the quality of sleep, research has been conducted on sleep disorder treatment in various fields including humanities, social studies, science, and engineering. In addition, data collection and pattern analysis of sleep have been researched and developed in detail. Although diverse sleep management products using IT convergence technology have been released, they only provide simple numbers or graphs of the personal sleep data measured. Therefore, this study proposes a mining-based time-series sleep pattern analysis of life big data. The proposed method collects and pre-processes sleep logs obtained continuously from life big data over time. As a result of pre-processing, sleep indexes comprise total sleep time, light sleep, deep sleep, awakening time, and bed time. External and internal factors that influence a sleep pattern are designed as context information, and then are normalized. In addition, context information is processed as a context index to create sleep data transactions. Based on the context index, similar users are identified based on similar context information. An expectation maximization algorithm is used to calculate maximum a posteriori through repetition of the expectation step, and a maximization step is applied for clustering. In sleep data transactions of a user set of a cluster, the AprioriAll algorithm of data mining is applied to create a sequential pattern of sleep in order to find meaningful associations, patterns, and trends. A pattern fitting a sleep stage is analyzed in order to improve sleep habits and treat sleep disorders. To improve REM sleep and ensure that a sufficient number of sleep hours are obtained, a mining-based time-series pattern is offered as feedback to each user. In this way, it is possible to improve the quality of sleep and life of a user.