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dc.contributor.authorH, Mohamed Muzamil
dc.date.accessioned2022-07-25T17:46:02Z
dc.date.available2022-07-25T17:46:02Z
dc.date.issued2022-07-25
dc.identifier.urihttp://hdl.handle.net/10222/81757
dc.descriptionExploratory tool to analyze mobile sensing data to identify behavioural patterns that may indicate mental health.en_US
dc.description.abstractMental well-being is increasingly demanded due to growing concerns about mental health. At the same time, the Internet and smartphones are transforming the world in unprecedented ways. This pervasiveness opens up new avenues for research by providing access to an individual’s behaviour and daily habits. Unobtrusive data collection and analysis from smartphone sensors is a promising approach to address- ing mental health issues and have been the focus of many research studies. In this work, we explore this opportunity by analyzing data collected from smartphone usage and leveraging the advantages of data visualization and machine learning methods to possibly identify and compare behavioural indicators and patterns that can indi- cate mental health. We developed a visualization system to interact with extracted features about behavioural indicators like screen usage, calling, and sleep to assess the daily routine of participants under study. We also present two usage scenarios to demonstrate our visual approach’s applicability in exploring the given dataset.en_US
dc.language.isoenen_US
dc.subjectVisual Analyticsen_US
dc.titleVISUAL ANALYSIS OF MOBILE SENSING TIME-SERIES DATA: IDENTIFYING INDIVIDUAL AND RELATIVE BEHAVIOURAL PATTERNSen_US
dc.date.defence2022-07-08
dc.contributor.departmentFaculty of Computer Scienceen_US
dc.contributor.degreeMaster of Computer Scienceen_US
dc.contributor.external-examinern/aen_US
dc.contributor.graduate-coordinatorMichael McAllisteren_US
dc.contributor.thesis-readerEvangelos Miliosen_US
dc.contributor.thesis-readerSandra Meieren_US
dc.contributor.thesis-supervisorFernando Paulovichen_US
dc.contributor.ethics-approvalNot Applicableen_US
dc.contributor.manuscriptsNot Applicableen_US
dc.contributor.copyright-releaseNot Applicableen_US
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