dc.contributor.author | Johansen, Sahand | |
dc.contributor.author | Johannessen, Tommy Sandtorv | |
dc.date.accessioned | 2019-11-04T08:19:24Z | |
dc.date.available | 2019-11-04T08:19:24Z | |
dc.date.issued | 2018 | |
dc.identifier.uri | http://hdl.handle.net/11250/2626258 | |
dc.description | Master's thesis Information- and communication technology IKT590 - University of Agder 2018 | nb_NO |
dc.description.abstract | Physical 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.iso | eng | nb_NO |
dc.publisher | Universitetet i Agder ; University of Agder | nb_NO |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/deed.no | * |
dc.subject | IKT590 | nb_NO |
dc.title | A Deep Learning Approach for Recognizing Daily Movement Patterns through Accelerometer Data | nb_NO |
dc.type | Master thesis | nb_NO |
dc.subject.nsi | VDP::Teknologi: 500::Informasjons- og kommunikasjonsteknologi: 550 | nb_NO |
dc.source.pagenumber | 80 p. | nb_NO |