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 to which they are attached. 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 language | English |
|---|---|
| Article number | 23 |
| Journal | ACM Transactions on Sensor Networks |
| Volume | 14 |
| Issue number | 3-4 |
| DOIs | |
| Publication status | Published - 2018 Dec |
Bibliographical note
Funding Information:This work is an extension to the paper “SenseTribute: Smart Home Occupant Identification via Fusion Across On-Object Sensing Devices,” published in the Proceedings of ACM International Conference on Systems for Energy-Efficient Built Environments (BuildSys) 2017. This work was supported in part by the National Science Foundation (under grants CNS-1645759, CNS-1149611 and CMMI-1653550), Intel, and Google. Authors’ addresses: J. Han, S. Pan, and M. K. Sinha, Carnegie Mellon University, Moffett Field, CA; emails: {junhan, shijiapan, manalkus}@cmu.edu; H. Y. Noh, Carnegie Mellon University, Pittsburgh, PA; email: noh@cmu.edu; P. Zhang and P. Tague, Carnegie Mellon University, Moffett Field, CA; emails: {peizhang, tague}@cmu.edu. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. © 2018 Association for Computing Machinery. 1550-4859/2018/12-ART23 $15.00 https://doi.org/10.1145/3218584
Funding Information:
This work is an extension to the paper "SenseTribute: Smart Home Occupant Identification via Fusion Across On-Object Sensing Devices," published in the Proceedings of ACM International Conference on Systems for Energy-Efficient Built Environments (BuildSys) 2017. This work was supported in part by the National Science Foundation (under grants CNS-1645759, CNS-1149611 and CMMI-1653550), Intel, and Google.
Publisher Copyright:
© 2018 Association for Computing Machinery.
All Science Journal Classification (ASJC) codes
- Computer Networks and Communications