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Unsupervised sctivity detection based on human range of motion features

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dc.contributor.authorOng, Wee-Hong
dc.contributor.authorKoseki, Takafumi
dc.date.accessioned2026-10-23T06:33:18Z
dc.date.available2026-10-23T06:33:18Z
dc.date.issued2013
dc.description.abstractHuman activity detection is usually achieved through supervised learning where a model is learned from given samples of each activity. With this approach, it is difficult for intelligent systems to automatically discover and learn new activities. We attempt to distinguish human activity using unsupervised learning. The approach relies on the fact that given appropriate features, the distinction between different activities can be observed from simple distance measurement. In this paper, we present our investigation to extract features from the coordinates of human joint positions based on human range of movement and the results of tests performed to check their effectiveness to distinguish sixteen (16) example activities are reported. Simple unsupervised learning, K means clustering was used to evaluate the effectiveness of the features. The results indicate that the features based on range of movement significantly improved clustering performance.
dc.format.extent5 pages
dc.identifier.urihttps://repository.ubd.edu.bn/handle/123456789/4787
dc.language.isoen
dc.sourceSeoul National University–University of Tokyo (SNU-UT) Joint Seminar
dc.subject.lcshHuman activity recognition
dc.subject.lcshCluster analysis
dc.subject.lcshMachine learning
dc.subject.lcshComputer vision
dc.subject.lcshPattern recognition systems
dc.titleUnsupervised sctivity detection based on human range of motion features
dc.typeArticle
dspace.entity.typePublication
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