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 posture recognition via online learning of multi-dimensional RFID received signal strength
AU - Yao, Lina
AU - Sheng, Quan Z.
AU - Ruan, Wenjie
AU - Li, Xue
AU - Wang, Sen
AU - Yang, Zhi
PY - 2016/1/15
Y1 - 2016/1/15
N2 - Activity recognition is a core component of ubiquitous computing applications (e.g., fall detection of elder people) since many of such applications require an intelligent environment to infer what a person is doing or attempting to do. Unfortunately, the success of existing approaches on activity recognition relies 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 paper, we propose a device-free, real-time posture recognition technique using an array of pure passive RFID tags. In particular, posture recognition is treated as a machine learning problem where a series of probabilistic model is built via learning how the Received Signal Strength Indicator (RSSI) from the tag array is distributed when a person performs different postures. We also design a segmentation algorithm to divide the continuous, multidimensional RSSI data stream into a set of individual segments by analyzing the shape of the RSSI data. Our approach for posture recognition eliminates the need for the monitored subjects to wear any devices. To the best of our knowledge, this work is the first on device-free posture recognition using low cost, unobtrusive RFID technology. Our experimental studies demonstrate the feasibility of the proposed approach for posture recognition.
AB - Activity recognition is a core component of ubiquitous computing applications (e.g., fall detection of elder people) since many of such applications require an intelligent environment to infer what a person is doing or attempting to do. Unfortunately, the success of existing approaches on activity recognition relies 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 paper, we propose a device-free, real-time posture recognition technique using an array of pure passive RFID tags. In particular, posture recognition is treated as a machine learning problem where a series of probabilistic model is built via learning how the Received Signal Strength Indicator (RSSI) from the tag array is distributed when a person performs different postures. We also design a segmentation algorithm to divide the continuous, multidimensional RSSI data stream into a set of individual segments by analyzing the shape of the RSSI data. Our approach for posture recognition eliminates the need for the monitored subjects to wear any devices. To the best of our knowledge, this work is the first on device-free posture recognition using low cost, unobtrusive RFID technology. Our experimental studies demonstrate the feasibility of the proposed approach for posture recognition.
U2 - 10.1109/ICPADS.2015.23
DO - 10.1109/ICPADS.2015.23
M3 - Conference contribution/Paper
SP - 116
EP - 123
BT - Proceedings - 2015 IEEE 21st International Conference on Parallel and Distributed Systems, ICPADS 2015
PB - IEEE
T2 - 21st IEEE International Conference on Parallel and Distributed Systems, ICPADS 2015
Y2 - 14 December 2015 through 17 December 2015
ER -