Human behavioral pattern analysis-based anomaly detection system in residential space

• Published in: The Journal of supercomputing Vol. 77; no. 8; pp. 9248 - 9265
• Main Authors: Choi, Seunghyun, Kim, Changgyun, Kang, Yong-Shin, Youm, Sekyoung
• Format: Journal Article
• Language: English
• Published: New York Springer US 2021; Springer Nature B.V
• Subjects: Algorithms; Anomalies; Behavior; Compilers; Computer Science; Deep learning; Human behavior; Interpreters; Machine learning; Methodology; Monitoring; Monitoring systems; Pattern analysis; Processor Architectures; Programming Languages; Soft Computing Technology in Artificial Intelligence; Deep learning; Monitoring system; Sequential pattern algorithm; Human behavioral analysis; Anomaly detection; Sequence alignment
• ISSN: 0920-8542; 1573-0484
• DOI: 10.1007/s11227-021-03641-7

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.