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Detecting anomalies in activity patterns of lone occupants from electricity consumption data Leong, Kuan Long

Abstract

As the global population is ageing, the demand for elderly care facilities and services is expected to increase. Assisted living technologies for detecting medical emergencies and assessing the wellness of the elderly are becoming more popular. A person normally performs activities of daily living (ADLs) on a regular basis. A person who is able to perform recurring ADLs indicates a certain wellness level. Anomalies in activity patterns of a person might indicate changes in the person's wellness. A method is proposed in this thesis for detecting anomalies in activity patterns of a lone occupant using electricity consumption measurements of his/her residence. The proposed method infers anomalies in activity patterns of an occupant from electricity consumption patterns without a need of explicitly monitoring the underlying individual activities. The proposed method provides a score which is a quantitative assessment of anomalies in the 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. The level of suspicion values, which are quantitative assessments of anomalies in the daily activity patterns of the occupants, were deduced from the survey. Using Fuzzy 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 based on the clustering results of the scores and level of suspicion values respectively. The results showed that anomalies in electricity consumption patterns can effectively reflect anomalies in the underlying activity patterns. The results also showed that the proposed feature and model based method outperforms a chosen raw data based approach. The performance of the proposed method was improved when subsets of features were considered based on the minimum Redundancy Maximum Relevance (mRMR) feature selection. A supervised learning method based on the Curious Extreme Learning Machine (C-ELM) was then proposed. The proposed method based on C-ELM (PM-CELM) outperforms the proposed method based on FCM (PM-FCM), but PM-FCM can operate without labelled training data.

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Attribution-NonCommercial-NoDerivs 2.5 Canada

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