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Activity recognition from acceleration data based on discrete consine transform and SVM

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2 Author(s)
Zhenyu He ; School of Electronic and Information Engineering, South China University of Technology, Guangzhou, China, 510640 ; Lianwen Jin

This paper developed a high-accuracy human activity recognition system based on single tri-axis accelerometer for use in a naturalistic environment. This system exploits the discrete cosine transform (DCT), the Principal Component Analysis (PCA) and Support Vector Machine (SVM) for classification human different activity. First, the effective features are extracted from accelerometer data using DCT. Next, feature dimension is reduced by PCA in DCT domain. After implementing the PCA, the most invariant and discriminating information for recognition is maintained. As a consequence, Multi-class Support Vector Machines is adopted to distinguish different human activities. Experiment results show that the proposed system achieves the best accuracy is 97.51%, which is better than other approaches.

Published in:

Systems, Man and Cybernetics, 2009. SMC 2009. IEEE International Conference on

Date of Conference:

11-14 Oct. 2009