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dc.contributor.authorJohansen, Sahand
dc.contributor.authorJohannessen, Tommy Sandtorv
dc.date.accessioned2019-11-04T08:19:24Z
dc.date.available2019-11-04T08:19:24Z
dc.date.issued2018
dc.identifier.urihttp://hdl.handle.net/11250/2626258
dc.descriptionMaster's thesis Information- and communication technology IKT590 - University of Agder 2018nb_NO
dc.description.abstractPhysical activity is a key factor in the treatment of chronic diseases such asdiabetes, cardiovascular disease, and depression. Doctors and personal trainershave limited methods to accurately monitor and classify a patients actual activi-ties based on training diaries and logs that are commonly used today. In this thesis,we apply a tri-axial accelerometer carried by a patient to collect data associated todifferent activities of daily life (ADL) and utilize deep learning (DL) algorithmsfor classifying distinct activities based on the data obtained from the accelerome-ter. Among various DL methods and algorithms, we adopt specifically deep neuralnetworks (DNN) and recurrent neural networks (RNN) to classify movement pat-terns. In addition, we compare our proposed structures with the state-of-the-artmethods via extensive experiments. Numerical results show that our proposedDNN model slightly exceeds, and our RNN model vastly outperforms the state-of-the-art methods in classification of basic movement patterns. The overall solu-tion for data collection and movement classification provides medical doctors andtrainers a promising way to precisely track and understand the physical activitiesof a patient for a better treatment.nb_NO
dc.language.isoengnb_NO
dc.publisherUniversitetet i Agder ; University of Agdernb_NO
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/deed.no*
dc.subjectIKT590nb_NO
dc.titleA Deep Learning Approach for Recognizing Daily Movement Patterns through Accelerometer Datanb_NO
dc.typeMaster thesisnb_NO
dc.subject.nsiVDP::Teknologi: 500::Informasjons- og kommunikasjonsteknologi: 550nb_NO
dc.source.pagenumber80 p.nb_NO


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Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal
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