A Novel Activity Recognition Approach Based on Mobile Phone
This paper presents a novel method for high-accuracy human activity recognition based on mobile phone acceleration sensors. Our approach includes two phases: the feature extraction phase and the classification phase. In feature extraction phase, we process tri-axial acceleration sensor data by combining the Independent Components Analysis (ICA) with the wavelet transform algorithm to get the features. In the classification phase, we apply the Support Vector Machine (SVM) algorithm to distinguish four types of activities (sitting, standing, walking and running). Experimental results show that the approach achieves an average accuracy of 98.78% over four types of activities, which outperforms the traditional method. The high accuracy indicates that this approach may facilitate the mobile phone based human activity recognition application.
Keywordsacceleration Independent Components Analysis wavelet transform SVM activity recognition
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