Smart-Energy Group Anomaly Based Behavioral Abnormality Detection

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dc.contributor.authorAlam, Mohammad Arif Ul
dc.contributor.authorRoy, Nirmalya
dc.contributor.authorPetruska, Michelle
dc.contributor.authorZemp, Andrea
dc.date.accessioned2018-09-04T18:10:17Z
dc.date.available2018-09-04T18:10:17Z
dc.date.issued2016-12-15
dc.description© 2016 IEEE; 2016 IEEE Wireless Health (WH)en_US
dc.description.abstractMonitoring behavioral abnormality of individuals living independently in their own homes is a key issue for building sustainable healthcare models in smart environments. While most of the efforts have been directed towards building ambient and wearable sensors-assisted activity recognition based behavioral analysis models for remote health monitoring, energy analytics assisted behavioral abnormality prediction have rarely been investigated. In this paper, we propose a data analytic approach that helps detect energy usage anomalies corresponding to the behavioral abnormality of the residents. Our approach relies on detecting everyday appliances usage from smart meter and smart plug data traces in regular activity days and then learning the unique time segment group of each appliance's energy consumption. We focus on detecting behavioral anomalies over a set of energy source data points rather than pinpointing individual odd points. We employ hierarchical probabilistic model-based group anomaly detection [7] to interpret the anomalous behavior and therefore, detect potential tendency towards behavioral abnormality. We apply daily activity logs to evaluate our approach using two realworld energy datasets pertaining to staged functional behaviors, and show that it is possible to detect max. 97% of anomalous days with max. 87% of meaningful micro-behavioral abnormal events generating 1.1% of false alarms. However, we show that our detected abnormality can be meaningfully represented to different stakeholders such as caregivers and family members to understand the nature and severity of abnormal human behavior for sustaining better healthcare.en_US
dc.description.sponsorshipThis work is supported partially by the NSF grant CNS1544687, ONR grant N00014-15-1-2229, and Constellation E2: Energy to Educate granten_US
dc.description.urihttps://ieeexplore.ieee.org/document/7764554/authorsen_US
dc.format.extent8 PAGESen_US
dc.genreconference papers and proceedings preprintsen_US
dc.identifierdoi:10.13016/M2DJ58K92
dc.identifier.citationAlam, Roy, Petruska and Zemp, "Smart-energy group anomaly based behavioral abnormality detection," 2016 IEEE Wireless Health (WH), Bethesda, MD, 2016, pp. 1-8.en_US
dc.identifier.uri10.1109/WH.2016.7764554
dc.identifier.urihttp://hdl.handle.net/11603/11207
dc.language.isoen_USen_US
dc.publisherIEEEen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Information Systems Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.rightsThis item may be protected under Title 17 of the U.S. Copyright Law. It is made available by UMBC for non-commercial research and education. For permission to publish or reproduce, please contact the author.
dc.subjectHome appliancesen_US
dc.subjectMonitoringen_US
dc.subjectEnergy consumptionen_US
dc.subjectSmart metersen_US
dc.subjectSmart homesen_US
dc.subjectMedical servicesen_US
dc.subjectPlugsen_US
dc.subjectMobile Pervasive & Sensor Computing Laben_US
dc.titleSmart-Energy Group Anomaly Based Behavioral Abnormality Detectionen_US
dc.typeTexten_US

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