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dc.contributor.authorZhang, Zelun
dc.date.accessioned2015-10-05T16:35:48Z
dc.date.available2015-10-05T16:35:48Z
dc.date.issued2014-06
dc.identifier.citationZhang, Z. 2014. User Mobility Detection using Foot Force Sensors and Mobile Phone GPS. Queen Mary University of London.en_US
dc.identifier.urihttp://qmro.qmul.ac.uk/jspui/handle/123456789/9116
dc.descriptionPhDen_US
dc.description.abstractA user (or human) mobility context is defined as a type of user context that describes a type of whole body posture (e.g., standing versus sitting) and/or a type of travel or transportation mode (e.g., walking, cycling, travel by bus, etc). Such a context can be derived from low-level sensor data and spatial contexts, including location coordinates, 3D-orientation, direction (with respect to magnetic north), velocity and acceleration. Different value-added services can be adapted to users’ mobility contexts such as assessing how eco-friendly our travel is, and adapting travel information services such as maps to different transportation modes. Current sensor-based methods for user mobility detection have several key limitations: narrow range of recognition, coarse user mobility recognition capability, and low recognition accuracy. In this thesis, a new Foot-Force and GPS (FF+GPS) sensor method is proposed to overcome these challenges that leverages a set of wearable FF sensors in combination with mobile phone GPS. The novelty of this approach is that it provides a more comprehensive recognition capability in terms of reliably recognising various fine-grained human postures and transportation modes. In addition, by comparing the new FF+GPS method with both an accelerometer (ACC) method (62% accuracy) and an ACC+GPS based method (70% accuracy) as baseline methods, it obtains a higher accuracy (90%) with less computational complexity, when tested on a dataset obtained from ten individuals. In addition, the new FF+GPS method has been further extended and evaluated. More specifically, the trade-off between the computation and resources needed to support lower versus higher number of features and sensors has been investigated. The improved FF+GPS method reduced the number of classification features from 31 to 12, reduced the number of FF sensors from 8 to 4, and reduced the use of GPS in mobility activity recognition.en_US
dc.language.isoenen_US
dc.publisherQueen Mary University of Londonen_US
dc.subjectElectronic Engineeringen_US
dc.subjectMobility recognitionen_US
dc.subjectFoot-force and GPS sensoren_US
dc.titleUser Mobility Detection using Foot Force Sensors and Mobile Phone GPS.en_US
dc.typeThesisen_US
dc.rights.holderThe copyright of this thesis rests with the author and no quotation from it or information derived from it may be published without the prior written consent of the author


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