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- W2488756812 abstract "This paper presents an effective detection algorithm for wrist motions based on an improved SVM (support vector machine) classifier. Firstly, a novel windowing method is proposed to enhance the consistency of sampled motion. After extracting characteristic features in both time and frequency domains, a feature scaling process is applied and a C-SVC (C-Support Vector Classifier) is trained by a threshold-based grid-search with cross-validation, to achieve higher accuracy and faster convergence. The experiments demonstrate that the proposed algorithm outperforms the traditional SVM in accuracy and convergence." @default.
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- W2488756812 date "2016-01-01" @default.
- W2488756812 modified "2023-09-23" @default.
- W2488756812 title "An Improved SVM-Based Motion Detection Algorithm Using an Accelerometer" @default.
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- W2488756812 doi "https://doi.org/10.1007/978-981-10-0740-8_13" @default.
- W2488756812 hasPublicationYear "2016" @default.
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