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

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.

[1]  W. Pearson Searching protein sequence libraries: comparison of the sensitivity and selectivity of the Smith-Waterman and FASTA algorithms. , 1991, Genomics.

[2]  Takehisa Yairi,et al.  An approach to spacecraft anomaly detection problem using kernel feature space , 2005, KDD '05.

[3]  Hayato Yamana,et al.  Generalized Sequential Pattern Mining with Item Intervals , 2006, J. Comput..

[4]  Ahmad Jalal,et al.  Wearable Sensor-Based Human Behavior Understanding and Recognition in Daily Life for Smart Environments , 2018, 2018 International Conference on Frontiers of Information Technology (FIT).

[5]  Bowen Du,et al.  Passenger Behavior Prediction With Semantic and Multi-Pattern LSTM Model , 2019, IEEE Access.

[6]  Wil M. P. van der Aalst,et al.  Process Mining - Discovery, Conformance and Enhancement of Business Processes , 2011 .

[7]  Mario Vento,et al.  To reject or not to reject: that is the question-an answer in case of neural classifiers , 2000, IEEE Trans. Syst. Man Cybern. Part C.

[8]  Marwan Hassani,et al.  Online conformance checking: relating event streams to process models using prefix-alignments , 2017, International Journal of Data Science and Analytics.

[9]  Johanna Völker,et al.  Discovery of Personal Processes from Labeled Sensor Data - An Application of Process Mining to Personalized Health Care , 2015, ATAED@Petri Nets/ACSD.

[10]  Muhammad Sajjad,et al.  Human Behavior Understanding in Big Multimedia Data Using CNN based Facial Expression Recognition , 2019, Mob. Networks Appl..

[11]  R. Saravanan,et al.  Link stable routing with minimal delay nodes for MANETs , 2018, Int. J. Soc. Humanist. Comput..

[12]  Boudewijn F. van Dongen,et al.  Event stream-based process discovery using abstract representations , 2017, Knowledge and Information Systems.

[13]  Ramakrishnan Srikant,et al.  Mining sequential patterns , 1995, Proceedings of the Eleventh International Conference on Data Engineering.

[14]  Xing Xie,et al.  Mining Individual Life Pattern Based on Location History , 2009, 2009 Tenth International Conference on Mobile Data Management: Systems, Services and Middleware.

[15]  K. Santacruz,et al.  Early diagnosis of dementia. , 2001, American family physician.

[16]  Ali Farhadi,et al.  You Only Look Once: Unified, Real-Time Object Detection , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[17]  Louis Kratz,et al.  Anomaly detection in extremely crowded scenes using spatio-temporal motion pattern models , 2009, CVPR.

[18]  Dorothy E. Denning,et al.  An Intrusion-Detection Model , 1987, IEEE Transactions on Software Engineering.

[19]  Diane J. Cook,et al.  Learning Setting-Generalized Activity Models for Smart Spaces , 2012, IEEE Intelligent Systems.

[20]  Thomas Seidl,et al.  Efficient Process Discovery From Event Streams Using Sequential Pattern Mining , 2015, 2015 IEEE Symposium Series on Computational Intelligence.

[21]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[22]  Boudewijn F. van Dongen,et al.  Replaying history on process models for conformance checking and performance analysis , 2012, WIREs Data Mining Knowl. Discov..

[23]  Zheng Xu The analytics and applications on supporting big data framework in wireless surveillance networks , 2017, Int. J. Soc. Humanist. Comput..

[24]  Hirokazu Seki Fuzzy inference based non-daily behavior pattern detection for elderly people monitoring system , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[25]  Rita Paradiso,et al.  WEALTHY – a wearable healthcare system: new frontier on e-textile , 2005, Journal of Telecommunications and Information Technology.

[26]  D. Mount Bioinformatics: Sequence and Genome Analysis , 2001 .

[27]  Qiming Chen,et al.  PrefixSpan,: mining sequential patterns efficiently by prefix-projected pattern growth , 2001, Proceedings 17th International Conference on Data Engineering.

[28]  Ruchuan Wang,et al.  TagCare: Using RFIDs to Monitor the Status of the Elderly Living Alone , 2017, IEEE Access.

[29]  Ting Yao,et al.  Human Behavior Understanding: From Action Recognition to Complex Event Detection , 2018, ACM Multimedia.

[30]  Marjorie Skubic,et al.  Fall Detection in Homes of Older Adults Using the Microsoft Kinect , 2015, IEEE Journal of Biomedical and Health Informatics.

[31]  Hussein A. Tahan,et al.  Motivational Interviewing: Building Rapport With Clients to Encourage Desirable Behavioral and Lifestyle Changes , 2012, Professional case management.

[32]  Han-Jin Cho,et al.  Hospital System Model for Personalized Medical Service , 2017 .

[33]  Weihua Sheng,et al.  Wearable Sensor-Based Behavioral Anomaly Detection in Smart Assisted Living Systems , 2015, IEEE Transactions on Automation Science and Engineering.

[34]  Li-Chen Fu,et al.  Context-aware activity prediction using human behavior pattern in real smart home environments , 2016, 2016 IEEE International Conference on Automation Science and Engineering (CASE).

[35]  Yoichi Sato,et al.  Learning motion patterns and anomaly detection by Human trajectory analysis , 2007, 2007 IEEE International Conference on Systems, Man and Cybernetics.

[36]  Guang-Zhong Cao,et al.  Effects of Different Feature Parameters of sEMG on Human Motion Pattern Recognition Using Multilayer Perceptrons and LSTM Neural Networks , 2020 .

[37]  Sekyoung Youm,et al.  Development of a methodology to predict and monitor emergency situations of the elderly based on object detection , 2018, Multimedia Tools and Applications.

[38]  Kwee-Bo Sim,et al.  Ring-type Heart Rate Sensor and Monitoring system for Sensor Network Application , 2007 .

[39]  Wil M. P. van der Aalst,et al.  Fuzzy Mining - Adaptive Process Simplification Based on Multi-perspective Metrics , 2007, BPM.

[40]  John A. Stankovic,et al.  Behavioral Patterns of Older Adults in Assisted Living , 2008, IEEE Transactions on Information Technology in Biomedicine.

[41]  Lee McGuigan,et al.  An impulse to exploit: the behavioral turn in data-driven marketing , 2018 .

[42]  Ross B. Girshick,et al.  Mask R-CNN , 2017, 1703.06870.

[43]  Leibo Liu,et al.  User Behavior Pattern Analysis and Prediction Based on Mobile Phone Sensors , 2010, NPC.

[44]  VARUN CHANDOLA,et al.  Anomaly detection: A survey , 2009, CSUR.

[45]  Juan Miguel García-Gómez,et al.  Process Mining for Individualized Behavior Modeling Using Wireless Tracking in Nursing Homes , 2013, Sensors.

[46]  Wei Liu,et al.  SSD: Single Shot MultiBox Detector , 2015, ECCV.