Infrastructure Engineering - Research Publications

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    User-Independent Motion State Recognition Using Smartphone Sensors
    Gu, F ; Kealy, A ; Khoshelham, K ; Shang, J (MDPI, 2015-12)
    The recognition of locomotion activities (e.g., walking, running, still) is important for a wide range of applications like indoor positioning, navigation, location-based services, and health monitoring. Recently, there has been a growing interest in activity recognition using accelerometer data. However, when utilizing only acceleration-based features, it is difficult to differentiate varying vertical motion states from horizontal motion states especially when conducting user-independent classification. In this paper, we also make use of the newly emerging barometer built in modern smartphones, and propose a novel feature called pressure derivative from the barometer readings for user motion state recognition, which is proven to be effective for distinguishing vertical motion states and does not depend on specific users' data. Seven types of motion states are defined and six commonly-used classifiers are compared. In addition, we utilize the motion state history and the characteristics of people's motion to improve the classification accuracies of those classifiers. Experimental results show that by using the historical information and human's motion characteristics, we can achieve user-independent motion state classification with an accuracy of up to 90.7%. In addition, we analyze the influence of the window size and smartphone pose on the accuracy.
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    APFiLoc: An Infrastructure-Free Indoor Localization Method Fusing Smartphone Inertial Sensors, Landmarks and Map Information
    Shang, J ; Gu, F ; Hu, X ; Kealy, A (MDPI, 2015-10)
    The utility and adoption of indoor localization applications have been limited due to the complex nature of the physical environment combined with an increasing requirement for more robust localization performance. Existing solutions to this problem are either too expensive or too dependent on infrastructure such as Wi-Fi access points. To address this problem, we propose APFiLoc-a low cost, smartphone-based framework for indoor localization. The key idea behind this framework is to obtain landmarks within the environment and to use the augmented particle filter to fuse them with measurements from smartphone sensors and map information. A clustering method based on distance constraints is developed to detect organic landmarks in an unsupervised way, and the least square support vector machine is used to classify seed landmarks. A series of real-world experiments were conducted in complex environments including multiple floors and the results show APFiLoc can achieve 80% accuracy (phone in the hand) and around 70% accuracy (phone in the pocket) of the error less than 2 m error without the assistance of infrastructure like Wi-Fi access points.
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    Starting to talk about place
    STIRLING, LESLEY ; CAVEDON, LAWRENCE ; RICHTER, DANIELA ; Winter, Stephen ; KEALY, ALLISON ; DUCKHAM, MATT ; RAJABIFARD, ABBAS ; RICHTER, KAI-FLORIAN ; Baldwin, Tim ( 2011)
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    Location-based mobile games for spatial knowledge acquisition
    Winter, S ; Richter, KF ; Baldwin, T ; Cavedon, L ; Stirling, L ; Duckham, M ; Kealy, A ; Rajabifard, A (CEMob2011, 2011-01-01)
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    Spatially Enabling 'Place' Information
    Winter, SW ; Bennett, RMB ; TRUELOVE, M ; Rajabifard, AR ; Duckham, MD ; Kealy, AK ; Leach, JHL ; Rajabifard, A ; Crompvoets, J ; Kalantari, M ; Kok, B (Leuven University Press, 2010)