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dc.contributor.authorDjumanov, Dilshatbek
dc.date.accessioned2011-12-08T14:14:37Z
dc.date.available2011-12-08T14:14:37Z
dc.date.issued2011
dc.identifier.urihttp://qmro.qmul.ac.uk/xmlui/handle/123456789/2355
dc.descriptionMPhilen_US
dc.description.abstractAutomation of data processing and control of operations involving intelligent technologies that is considered the next generation technology requires error-free data capture systems in both clinical research and healthcare. The presented research constitutes a step in the development of intelligent technologies in healthcare. The proposed improvement is by automation that includes the elements of intelligence and prediction. In particular automatic data acquisition systems for several devices are developed including pervasive computing technologies for mobility. The key feature of the system is the minimisation/near eradication of erroneous data input along with a number of other security measures ensuring completeness, accuracy and reliability of the patients‟ data. The development is based on utilising existing devices to keep the cost of Data Acquisition Systems down. However, with existing technology and devices one can be limited to features required to perform more refined analysis. Research of existing and development of a new device for assessment of neurological diseases, such as MS (Multiple Sclerosis) using Stroop test is performed. The software can also be customized for use in other diseases affecting Central Nervous System such as Parkinson‟s disease. The introduction of intelligent functions into the majority of operations enables quality checks and provides on-line user assistance. It could become a key tool in the first step of patient diagnosis before referring to more advanced tests for further investigation. Although the software cannot fully ensure the diagnosis of MS or PD but can make significant contribution in the process of diagnosis and monitoringen_US
dc.language.isoenen_US
dc.subjectEngineeringen_US
dc.titleIntelligent technologies for real-time monitoring and decision support systemsen_US
dc.typeThesisen_US


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  • Theses [3366]
    Theses Awarded by Queen Mary University of London

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