A User-Centered Active Learning Approach for Appliance Recognition

Eura Shin, Atieh R. Khamesi, Zachary Bahr, Simone Silvestri, D. A. Baker

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations


Smart homes offer new possibilities for energy management. One key enabler of these systems is the ability to monitor energy consumption at the appliance level. Existing approaches rely mainly on data from aggregated smart meter readings, but lack sufficient accuracy to recognize several appliances. Conversely, smart outlets are a suitable alternative since they can provide accurate electrical readings on individual appliances. Previous approaches for appliance recognition based on smart outlets use passive machine learning, which are deficient in the flexibility and scalability to work with highly heterogeneous appliances in smart homes. In this paper, we propose a stream-based active learning approach, called K-Active-Neighbors (KAN), to address the problem of appliance recognition in smart homes. KAN is an interactive framework in which the user is asked to label signatures of recently used appliances. Differently from previous work, we consider the realistic case in which the user is not always available to participate in the labeling process. Therefore, the system simultaneously learns the signatures and also the user willingness to interact with the system, in order to optimize the learning process. We develop an Arduino-based smart outlet to test our approach. Results show that, compared to previous solutions, KAN achieves higher accuracy in up to 41% less time.

Original languageEnglish
Title of host publicationProceedings - 2020 IEEE International Conference on Smart Computing, SMARTCOMP 2020
Number of pages6
ISBN (Electronic)9781728169972
StatePublished - Sep 2020
Event6th IEEE International Conference on Smart Computing, SMARTCOMP 2020 - Virtual, Bologna, Italy
Duration: Sep 14 2020Sep 17 2020

Publication series

NameProceedings - 2020 IEEE International Conference on Smart Computing, SMARTCOMP 2020


Conference6th IEEE International Conference on Smart Computing, SMARTCOMP 2020
CityVirtual, Bologna

Bibliographical note

Funding Information:
This work is supported by the National Institute for Food and Agriculture (NIFA) under the grant 2017-67008-26145, the NSF grant EPCN 1936131, and the NSF CAREER grant CPS-1943035.

Publisher Copyright:
© 2020 IEEE.


  • Appliance Recognition
  • Labeler Abstention
  • Stream-based Active learning
  • User-Centered Machine Learning

ASJC Scopus subject areas

  • Computer Science Applications
  • Information Systems and Management
  • Artificial Intelligence
  • Computer Networks and Communications


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