Smart-energy group anomaly based behavioral abnormality detection

Monitoring 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.