NDLI: Unobtrusive Posture Recognition via Online Learning of Multi-dimensional RFID Received Signal Strength

Content Provider	IEEE Xplore Digital Library
Author	Lina Yao Sheng, Q.Z. Wenjie Ruan Xue Li Sen Wang Zhi Yang
Copyright Year	2015
Description	Author affiliation: Sch. of Comput. Sci., Univ. of Adelaide, Adelaide, SA, Australia (Lina Yao; Sheng, Q.Z.; Wenjie Ruan; Zhi Yang) \|\| Sch. of ITEE, Univ. of Queensland, Brisbane, QLD, Australia (Xue Li; Sen Wang)
Abstract	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.
Starting Page	116
Ending Page	123
File Size	474076
Page Count	8
File Format	PDF
ISSN	15219097
e-ISBN	9780769557854
DOI	10.1109/ICPADS.2015.23
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-12-14
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Passive RFID tags Arrays Support vector machines Received signal strength indicator Monitoring
Content Type	Text
Resource Type	Article
Subject	Hardware and Architecture

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