Unobtrusive posture recognition via online learning of multi-dimensional RFID received signal strength

Yao, Lina and Sheng, Quan Z. and Ruan, Wenjie and Li, Xue and Wang, Sen and Yang, Zhi (2016) Unobtrusive posture recognition via online learning of multi-dimensional RFID received signal strength. In: Proceedings - 2015 IEEE 21st International Conference on Parallel and Distributed Systems, ICPADS 2015. IEEE, AUS, pp. 116-123. ISBN 9780769557854

Full text not available from this repository.


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.

Item Type:
Contribution in Book/Report/Proceedings
Uncontrolled Keywords:
ID Code:
Deposited By:
Deposited On:
22 Jun 2019 00:59
Last Modified:
17 Sep 2023 04:04