TY - GEN
T1 - E-eyes
T2 - 20th ACM Annual International Conference on Mobile Computing and Networking, MobiCom 2014
AU - Wang, Yan
AU - Liu, Jian
AU - Chen, Yingying
AU - Gruteser, Marco
AU - Yang, Jie
AU - Liu, Hongbo
N1 - Publisher Copyright:
© 2014 by the Association for Computing Machinery, Inc. (ACM).
PY - 2014/9/7
Y1 - 2014/9/7
N2 - Activity monitoring in home environments has become increasingly important and has the potential to support a broad array of applications including elder care, well-being management, and latchkey child safety. Traditional approaches involve wearable sensors and specialized hardware installations. This paper presents device-free location-oriented activity identification at home through the use of existing WiFi access points and WiFi devices (e.g., desktops, thermostats, refrigerators, smartTVs, laptops). Our low-cost system takes advantage of the ever more complex web of WiFi links between such devices and the increasingly fine-grained channel state information that can be extracted from such links. It examines channel features and can uniquely identify both in-place activities and walking movements across a home by comparing them against signal profiles. Signal profiles construction can be semisupervised and the profiles can be adaptively updated to accommodate the movement of the mobile devices and day-to-day signal calibration. Our experimental evaluation in two apartments of different size demonstrates that our approach can achieve over 96% average true positive rate and less than 1% average false positive rate to distinguish a set of in-place and walking activities with only a single WiFi access point. Our prototype also shows that our system can work with wider signal band (802.11ac) with even higher accuracy.
AB - Activity monitoring in home environments has become increasingly important and has the potential to support a broad array of applications including elder care, well-being management, and latchkey child safety. Traditional approaches involve wearable sensors and specialized hardware installations. This paper presents device-free location-oriented activity identification at home through the use of existing WiFi access points and WiFi devices (e.g., desktops, thermostats, refrigerators, smartTVs, laptops). Our low-cost system takes advantage of the ever more complex web of WiFi links between such devices and the increasingly fine-grained channel state information that can be extracted from such links. It examines channel features and can uniquely identify both in-place activities and walking movements across a home by comparing them against signal profiles. Signal profiles construction can be semisupervised and the profiles can be adaptively updated to accommodate the movement of the mobile devices and day-to-day signal calibration. Our experimental evaluation in two apartments of different size demonstrates that our approach can achieve over 96% average true positive rate and less than 1% average false positive rate to distinguish a set of in-place and walking activities with only a single WiFi access point. Our prototype also shows that our system can work with wider signal band (802.11ac) with even higher accuracy.
KW - Activity recognition
KW - Channel state information (CSI)
KW - Device- free
KW - Location-oriented
KW - WiFi
UR - http://www.scopus.com/inward/record.url?scp=84907817476&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84907817476&partnerID=8YFLogxK
U2 - 10.1145/2639108.2639143
DO - 10.1145/2639108.2639143
M3 - Conference contribution
AN - SCOPUS:84907817476
T3 - Proceedings of the Annual International Conference on Mobile Computing and Networking, MOBICOM
SP - 617
EP - 628
BT - MobiCom 2014 - Proceedings of the 20th Annual
PB - Association for Computing Machinery
Y2 - 7 September 2014 through 11 September 2014
ER -