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- W2004687920 abstract "This paper describes how to use a posture sensor to validate human daily activity and by machine learning algorithm - Support Vector Machine (SVM) an outstanding model is built. The optimal parameter σ and c of RBF kernel SVM were obtained by searching automatically. Those kinematic data was carried out through three major steps: wavelet transformation, Principle Component Analysis (PCA) -based dimensionality reduction and k-fold cross-validation, followed by implementing a best classifier to distinguish 6 difference actions. As an activity classifier, the SVM (Support Vector Machine) algorithm is used, and we have achieved over 94.5% of mean accuracy in detecting differential actions. It shows that the verification approach based on the recognition of human activity detection is valuable and will be further explored in the near future." @default.
- W2004687920 created "2016-06-24" @default.
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- W2004687920 date "2012-02-01" @default.
- W2004687920 modified "2023-09-24" @default.
- W2004687920 title "Recognition and Classification of Human Activity by Posture Sensing and Machine Learning" @default.
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- W2004687920 doi "https://doi.org/10.4028/www.scientific.net/amr.468-471.2916" @default.
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