Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
TY - GEN
T1 - Unobtrusive human localization and activity recognition for supporting independent living of the elderly
AU - Ruan, Wenjie
PY - 2016/3/14
Y1 - 2016/3/14
N2 - Indoor localization and activity recognition is a fundamental research topic for a wide range of important applications such as fall detection of elderly people. It usually requires an intelligent environment to successfully infer where and what a person is doing. However, many of the existing techniques on localization and activity recognition rely heavily on people's involvement such as wearing battery-powered sensors, which might not be practical in real-world situations (e.g., people may forget to wear sensors). In this project, we propose a device-free localization and activity recognition approach using passive RFID tags. It is achieved by learning how the Received Signal Strength Indicator (RSSI) from the passive RFID tag array is distributed when a person performs different activities in different locations. After activity patterns are discovered for a particular individual, we will also develop a context-aware, common-sense based activity reasoning engine that assists applications to make appropriate interpretation of detected activities. We believe the proposed system has the potential to better support the independent living of elderly people considering the continuously increased aging population.
AB - Indoor localization and activity recognition is a fundamental research topic for a wide range of important applications such as fall detection of elderly people. It usually requires an intelligent environment to successfully infer where and what a person is doing. However, many of the existing techniques on localization and activity recognition rely heavily on people's involvement such as wearing battery-powered sensors, which might not be practical in real-world situations (e.g., people may forget to wear sensors). In this project, we propose a device-free localization and activity recognition approach using passive RFID tags. It is achieved by learning how the Received Signal Strength Indicator (RSSI) from the passive RFID tag array is distributed when a person performs different activities in different locations. After activity patterns are discovered for a particular individual, we will also develop a context-aware, common-sense based activity reasoning engine that assists applications to make appropriate interpretation of detected activities. We believe the proposed system has the potential to better support the independent living of elderly people considering the continuously increased aging population.
U2 - 10.1109/PERCOMW.2016.7457085
DO - 10.1109/PERCOMW.2016.7457085
M3 - Conference contribution/Paper
AN - SCOPUS:84966470322
SP - 1
EP - 3
BT - 2016 IEEE International Conference on Pervasive Computing and Communication Workshops, PerCom Workshops 2016
PB - IEEE
T2 - 13th IEEE International Conference on Pervasive Computing and Communication Workshops, PerCom Workshops 2016
Y2 - 14 March 2016 through 18 March 2016
ER -