Increasingly, research has analyzed human behavior in various fields. The fourth industrial revolution technology is very useful for analyzing human behavior. From the viewpoint of the residential space monitoring system, the life patterns in human living spaces vary widely, and it is very difficult to find abnormal situations. Therefore, this study proposes a living space-based monitoring system. The system includes the behavioral analysis of monitored subjects using a deep learning methodology, behavioral pattern derivation using the PrefixSpan algorithm, and the anomaly detection technique using sequence alignment. Objectivity was obtained through behavioral recognition using deep learning rather than subjective behavioral recording, and the time to derive a pattern was shortened using the PrefixSpan algorithm among sequential pattern algorithms. The proposed system provides personalized monitoring services by applying the methodology of other fields to human behavior. Thus, the system can be extended using another methodology or fourth industrial revolution technology.

越来越多的研究分析了各个领域的人类行为。第四次工业革命技术对于分析人类行为非常有用。从住宅空间监控系统的角度来看，人类居住空间的生活模式差异很大，很难发现异常情况。因此，本研究提出了一种基于居住空间的监视系统。该系统包括使用深度学习方法对受监视对象进行的行为分析，使用PrefixSpan算法的行为模式推导以及使用序列比对的异常检测技术。客观性是通过使用深度学习而不是主观的行为记录通过行为识别而获得的，在顺序模式算法中，使用PrefixSpan算法可以缩短生成模式的时间。所提出的系统通过将其他领域的方法应用于人类行为来提供个性化的监视服务。因此，可以使用另一种方法或第四次工业革命技术来扩展该系统。