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Human activity recognition with wireless sensor networks using machine learning

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dc.contributor Ph.D. Program in Computer Engineering.
dc.contributor.advisor Ersoy, Cem.
dc.contributor.author Alemdar, Hande.
dc.date.accessioned 2023-03-16T10:13:42Z
dc.date.available 2023-03-16T10:13:42Z
dc.date.issued 2015.
dc.identifier.other CMPE 2015 A44 PhD
dc.identifier.uri http://digitalarchive.boun.edu.tr/handle/123456789/12597
dc.description.abstract Recognizing human behavior in an automated manner is essential in many ambient intelligence applications such as smart homes, health monitoring applications and emergency services. In order to make such long term health monitoring systems sustainable, we need smart environments in which the human activities are recognized automatically. In order to infer the human behavior, we can use machine learning methods on the data collected from the smart environments but those methods require annotated datasets to be trained on. Recording and annotating such datasets are costly since they require time and human e ort. Moreover, the complex nature of human activities makes it di cult to accurately model them. While hierarchical models can be a remedy for more accurate representation, nding suitable complexity levels is not a trivial task. Finally, when we deploy automatic human behavior monitoring systems on a world-wide scale, we need to ne tune the model behavior for each new house to accurately re ect the residents' behavior for that speci c house. Rather than annotating a dataset consisting of several weeks of data, an algorithm can be used to decide for which point in time it would be most informative to obtain annotation in order to minimize the need for annotation and maximize the usefulness of annotation. This thesis addresses the above mentioned issues by (i) collecting publicly available benchmark datasets, (ii) proposing a methodology for incorporating a hierarchy into the model that is tailored for various activities individually, (iii) improving the ways of evaluating di erent approaches and models considering the domain speci c needs, (iv) handling multi-resident environments in an unobtrusive manner and, (v) using active and semi-supervised learning techniques in order to reduce the annotation e ort in large scale deployments.
dc.format.extent 30 cm.
dc.publisher Thesis (Ph.D.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2015.
dc.subject.lcsh Wireless sensor networks.
dc.title Human activity recognition with wireless sensor networks using machine learning
dc.format.pages xviii, 135 leaves ;


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