Pedestrian Motion Modes Recognition of Smart Phone Based on Support Vector Machine

doi:10.13474/j.cnki.11-2246.2018.0033

Abstract

Abstract:

It is an important method in the field of intelligent recognition to use the built-in multiple integration sensors of smart phones as data acquisition tools,and to identify pedestrian motion modes combined with support vector machine(SVM). In this paper,walking,up/down stairs,up/down elevator,up/down escalator are selected as the common indoor motion modes.After effective features are selected,the linear kernel function SVM is used to establish the classification model to recognize the test data.The results show that 13 features are extracted from the data collected from pedestrian motion modes,and 96.4% recognition accuracy can be obtained at the 3 s data sliding window with 0.5 s data sampling interval.

Key words: motion modes, support vector machine, classification, recognition

CLC Number:

P228

GUO Jiming, WANG Wei, ZHANG Shuai. Pedestrian Motion Modes Recognition of Smart Phone Based on Support Vector Machine[J]. 测绘通报, 2018, 0(2): 1-5.

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