SenseTribute: Smart Home Occupant Identification via Fusion Across On-Object Sensing Devices

Jun Han, Hae Young Noh, Shijia Pan, Pei Zhang, Manal Kumar Sinha, Patrick Tague

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

Occupant identification proves crucial in many smart home applications such as automated home control and activity recognition. Previous solutions are limited in terms of deployment costs, identification accuracy, or usability. We propose SenseTribute, a novel occupant identification solution that makes use of existing and prevalent on-object sensors that are originally designed to monitor the status of objects they are attached to. SenseTribute extracts richer information content from such on-object sensors and analyzes the data to accurately identify the person interacting with the objects. This approach is based on the physical phenomenon that different occupants interact with objects in different ways. Moreover, SenseTribute may not rely on users’ true identities, so the approach works even without labeled training data. However, resolution of information from a single on-object sensor may not be sufficient to differentiate occupants, which may lead to errors in identification. To overcome this problem, SenseTribute operates over a sequence of events within a user activity, leveraging recent work on activity segmentation. We evaluate SenseTribute using real-world experiments by deploying sensors on five distinct objects in a kitchen and inviting participants to interact with the objects. We demonstrate that SenseTribute can correctly identify occupants in 96% of trials without labeled training data, while per-sensor identification yields only 74% accuracy even with training data.

Original languageEnglish
Title of host publicationBuildSys 2017 - Proceedings of the 4th ACM International Conference on Systems for Energy-Efficient Built Environments
EditorsRasit Eskicioglu
PublisherAssociation for Computing Machinery, Inc
ISBN (Electronic)9781450354769
DOIs
Publication statusPublished - 2017 Nov 8
Event4th ACM International Conference on Systems for Energy-Efficient Built Environments, BuildSys 2017 - Delft, Netherlands
Duration: 2017 Nov 82017 Nov 9

Publication series

NameBuildSys 2017 - Proceedings of the 4th ACM International Conference on Systems for Energy-Efficient Built Environments
Volume2017-January

Conference

Conference4th ACM International Conference on Systems for Energy-Efficient Built Environments, BuildSys 2017
Country/TerritoryNetherlands
CityDelft
Period17/11/817/11/9

Bibliographical note

Funding Information:
This research was supported in part by the National Science Foundation (under grants CNS-1645759, CNS-1149611 and CMMI-1653550), Intel and Google. The views and conclusions contained here are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either express or implied, of CMU, NSF, or the U.S. Government or any of its agencies.

Publisher Copyright:
© 2017 Association for Computing Machinery.

All Science Journal Classification (ASJC) codes

  • Architecture
  • Renewable Energy, Sustainability and the Environment
  • Electrical and Electronic Engineering
  • Computer Networks and Communications
  • Building and Construction
  • Information Systems

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