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.
|Title of host publication||BuildSys 2017 - Proceedings of the 4th ACM International Conference on Systems for Energy-Efficient Built Environments|
|Publisher||Association for Computing Machinery, Inc|
|Publication status||Published - 2017 Nov 8|
|Event||4th ACM International Conference on Systems for Energy-Efficient Built Environments, BuildSys 2017 - Delft, Netherlands|
Duration: 2017 Nov 8 → 2017 Nov 9
|Name||BuildSys 2017 - Proceedings of the 4th ACM International Conference on Systems for Energy-Efficient Built Environments|
|Conference||4th ACM International Conference on Systems for Energy-Efficient Built Environments, BuildSys 2017|
|Period||17/11/8 → 17/11/9|
Bibliographical noteFunding 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.
© 2017 Association for Computing Machinery.
All Science Journal Classification (ASJC) codes
- Renewable Energy, Sustainability and the Environment
- Electrical and Electronic Engineering
- Computer Networks and Communications
- Building and Construction
- Information Systems