Towards user identification in the home from appliance usage patterns.
UbiComp '13: The 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing Zurich Switzerland September, 2013(2013)
摘要
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%.
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