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- W655913327 abstract "The ability to track the location of a subject in their home allows the provision of anumber of location based services, such as remote activity monitoring, context sensitiveprompts and detection of safety critical situations such as falls. Such pervasive monitoringfunctionality offers the potential for elders to live at home for longer periods of their liveswith minimal human supervision.The focus of this thesis is on the investigation and development of a home roomlevellocalisation technique which can be readily deployed in a realistic home environmentwith minimal hardware requirements. A conveniently deployed Bluetooth ® localisationplatform is designed and experimentally validated throughout the thesis. The platformadopts the convenience of a mobile phone and the processing power of a remote locationcalculation computer. The use of Bluetooth ® also ensures the extensibility of the platformto other home health supervision scenarios such as wireless body sensor monitoring.Central contributions of this work include the comparison of probabilistic and nonprobabilisticclassifiers for location prediction accuracy and the extension of probabilisticclassifiers to a Hidden Markov Model Bayesian filtering framework. New locationprediction performance metrics are developed and signicant performance improvementsare demonstrated with the novel extension of Hidden Markov Models to higher-orderMarkov movement models. With the simple probabilistic classifiers, location is correctlypredicted 80% of the time. This increases to 86% with the application of the HiddenMarkov Models and 88% when high-order Hidden Markov Models are employed.Further novelty is exhibited in the derivation of a real-time Hidden Markov ModelViterbi decoding algorithm which presents all the advantages of the original algorithm,while producing location estimates in real-time. Significant contributions are also madeto the field of human gait-recognition by applying Bayesian filtering to the task of motiondetection from accelerometers which are already present in many mobile phones. Bayesian filtering is demonstrated to enable a 35% improvement in motion recognition rate and evenenables a floor recognition rate of 68% using only accelerometers. The unique applicationof time-varying Hidden Markov Models demonstrates the effect of integrating these freelyavailable motion predictions on long-term location predictions." @default.
- W655913327 created "2016-06-24" @default.
- W655913327 creator A5002286841 @default.
- W655913327 date "2010-02-01" @default.
- W655913327 modified "2023-09-23" @default.
- W655913327 title "Minimal Infrastructure RadioFrequency Home LocalisationSystems" @default.
- W655913327 hasPublicationYear "2010" @default.
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