Many smart home applications, such as monitoring for the elderly and home automation, require location information for individual occupants. Several techniques have been proposed for tracking occupants in a home environment. However, the current techniques do not provide a seamless in-home locating system owing to the occupants' devicefree movement and the lack of cost-effective infrastructure for home location tracking. In this paper, we propose a home occupant tracking system that uses a smartphone and an off-The-shelf smartwatch without additional infrastructure. In our system, activity fingerprints are automatically generated from the microphone and the inertial sensors of the smartwatch, and location information is periodically obtained from the smartphone. We designed a hidden Markov model using the relationship between home activities and the room's location. Extensive experiments showed that our system tracks the location of users with 87% accuracy, even when there is no manual training for activities.
|Title of host publication||UbiComp 2015 - Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing|
|Publisher||Association for Computing Machinery, Inc|
|Number of pages||12|
|Publication status||Published - 2015 Sep 7|
|Event||3rd ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2015 - Osaka, Japan|
Duration: 2015 Sep 7 → 2015 Sep 11
|Name||UbiComp 2015 - Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing|
|Other||3rd ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2015|
|Period||15/9/7 → 15/9/11|
Bibliographical notePublisher Copyright:
© ACM 978-1-4503-3574-4/15/09..15.00.
All Science Journal Classification (ASJC) codes
- Computer Networks and Communications
- Hardware and Architecture