User-friendly Activity Recognition Using SVM Classifier and Informative Features

被引：0

作者：

Phong Nguyen ^{[1
]}

Akiyama, Takayuki ^{[1
]}

Ohashi, Hiroki ^{[1
]}

Nakahara, Goh ^{[2
]}

Yamasaki, Katsuya ^{[2
]}

Hikaru, Saito ^{[2
]}

机构：

[1] Hitachi Ltd, Ctr Technol Innovat Syst Engn, Tokyo, Japan

[2] Hitachi Adv Syst Co Ltd, Yokohama, Kanagawa, Japan

来源：

2015 INTERNATIONAL CONFERENCE ON INDOOR POSITIONING AND INDOOR NAVIGATION (IPIN) | 2015年

关键词：

indoor positioning; activity recognition; smartphone; machine learning; PEDESTRIAN TRACKING;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

For accurate indoor positioning, a moving activity recognition (AR) method has been developed that is based on a smartphone's sensor data. Prior methods can only recognize moving activities if the smartphone is held in a predefined place. We propose a method that works in various holding places to increase the usability. An SVM classifier is chosen because of its strength in utilizing features. New features are added such as percentiles of acceleration, air pressure, and acceleration magnitude. We have achieved 94.3% overall accuracy in various holding places: in users' hands, belt pouches, pant back pockets, and pant side pockets.

引用

页数：8

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