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An activity recognition algorithm based on multi-feature fuzzy cluster. (English) Zbl 1420.62278

Jia, Yingmin (ed.) et al., Proceedings of the 2015 Chinese intelligent systems conference, CISC’15, Yangzhou, China. Volume 2. Berlin: Springer. Lect. Notes Electr. Eng. 360, 363-375 (2016).
Summary: In this paper an activity recognition algorithm based on multi-feature fuzzy cluster is designed to find out more details of the activities so as to achieve an accurate classification among them. Firstly, it is proved that distribution of feature vectors vary from activity to activity. And then, a multi-feature extraction algorithm is designed to extract the feature vectors of each activity which makes up a standard activity class. Finally, an activity recognition algorithm based on similarity measurement is brought up and the misjudgment rate turns out to be acceptable, which proves that this algorithm is accurate and highly feasible.
For the entire collection see [Zbl 1337.93002].

MSC:

62H30 Classification and discrimination; cluster analysis (statistical aspects)
62H86 Multivariate analysis and fuzziness
62P10 Applications of statistics to biology and medical sciences; meta analysis
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