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

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Publication date15/01/2016
Host publicationProceedings - 2015 IEEE 21st International Conference on Parallel and Distributed Systems, ICPADS 2015
PublisherIEEE
Pages116-123
Number of pages8
ISBN (Electronic)9780769557854
<mark>Original language</mark>English
Event21st IEEE International Conference on Parallel and Distributed Systems, ICPADS 2015 - Melbourne, Australia
Duration: 14/12/201517/12/2015

Conference

Conference21st IEEE International Conference on Parallel and Distributed Systems, ICPADS 2015
Country/TerritoryAustralia
CityMelbourne
Period14/12/1517/12/15

Conference

Conference21st IEEE International Conference on Parallel and Distributed Systems, ICPADS 2015
Country/TerritoryAustralia
CityMelbourne
Period14/12/1517/12/15

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.