Unsupervised Monitoring Of Electrical Devices For Detecting Deviations In Daily Routines

This paper presents a novel approach for automatic detection of abnormal behaviours in daily routine of people living alone in their homes, without any manual labelling of the training dataset. Regularity and frequency of activities are monitored by estimating the status of specific electrical appliances via their power signatures identified from the composite power signal of the house. A novel unsupervised clustering technique is presented to automatically profile the power signatures of electrical devices. Then, the use of a test statistic is proposed to distinguish power signatures resulted from the occupant interactions from those of self-regulated appliances such as refrigerator. Experiments on real-world data showed the effectiveness of the proposed approach in terms of detection of the occupant's interactions with appliances as well as identifying those days that the behaviour of the occupant was outside the normal pattern.