Towards user identification in the home from appliance usage patterns

Authors:
Daniel Garnier-Moiroux;Fernando Silveira;Anmol Sheth
Affiliations:
Mines ParisTech, Paris, France;Technicolor, Palo Alto, USA;Technicolor, Palo Alto, CA, USA
Venue:
Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication
Year:
2013

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Abstract

We explore the feasibility of identifying users from the unique patterns they exhibit when interacting with an individual electrical appliance in the home. We evaluate the effectiveness of a supervised learning based approach for user identification from a dataset of appliance usage collected across five users and three kitchen appliances over a period of eight weeks. Our results show that using appliance usage information alone provides a moderate average accuracy of 32% for group sizes of up to five users in the home. However augmenting usage information with hints about user presence can improve accuracy by 15-20%.