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Detection of Anomalies in Activity Patterns of Lone Occupants from Electricity Usage Data


Authors: K. Leong, C. Leung, C. Miao, and Y. C. Chen
Title: Detection of Anomalies in Activity Patterns of Lone Occupants from Electricity Usage Data
Abstract: As the global population ages, assisted living technologies for the elderly are becoming more popular. A person normally performs activities of daily living (ADLs) on a regular basis. A person’s ability to perform recurring ADLs indicates the person’s wellness. Anomalies in activity patterns of a person might indicate changes in the person’s wellness. A method is proposed in this paper for detecting anomalies in activity patterns of a lone occupant using his/her electricity consumption data. The proposed method infers anomalies in activity patterns of an occupant from electricity consumption patterns instead of explicitly monitoring the underlying individual activities. The proposed method provides a score which is a quantitative assessment of anomalies in electricity consumption pattern of an occupant for a given day. A survey was conducted to obtain the hourly activities of three lone occupants for a month. From the survey, the level of suspicion values which are quantitative assessments of anomalies in activity patterns of the occupants were deduced. Using Fuzzy C-C- Means (FCM) clustering with Euclidean distance measure, the scores and level of suspicion values were clustered respectively. A day was then classified as regular or irregular from an electricity consumption perspective (score) and an activity perspective (level of suspicion value) respectively. Our results show that anomalies in electricity consumption patterns correlate well with anomalies in the underlying activity patterns.
Keywords: 
Conference Name: IEEE Congress on Evolutionary Computation (CEC’16)
Location: Vancouver, Canada
Publisher: IEEE
Year: 2016
Accepted PDF File: Detection_of_Anomalies_in_Activity_Patterns_of_Lone_Occupants_from_Electricity_Usage_Data_accepted.pdf
Permanent Link: https://dx.doi.org/10.1109/CEC.2016.7743947
Reference: K. Leong, C. Leung, C. Miao, and Y. C. Chen, “Detection of anomalies in activity patterns of lone occupants from electricity usage data,” in Proceedings of the IEEE Congress on Evolutionary Computation (CEC’16). IEEE, July 2016, pp. 1361–1369.
bibtex: 
@inproceedings{LILY-c88, 
    author	= {Leong, Kuanlong and Leung, Cyril and Miao, Chunyan and Chen, Yu Christine},
    title	= {Detection of Anomalies in Activity Patterns of Lone Occupants from Electricity Usage Data},  
    booktitle	= {Proceedings of the IEEE Congress on Evolutionary Computation (CEC'16)}, 
    year		= {2016}, 
    month	= {July}, 
    pages	= {1361-1369}, 
    location	= {Vancouver, Canada},
    publisher	= {IEEE},
 }