Recognizing Human Activities in Real-Time Using Mobile Phone Sensors

被引：1

作者：

Jia, Boxuan ^{[1
]}

Li, Jinbao ^{[2
]}

机构：

[1] Heilongjiang Univ, Sch Comp Sci & Technol, Harbin 150080, Heilongjiang, Peoples R China

[2] Key Lab Database & Parallel Comp Heilongjiang Pro, Harbin 150080, Heilongjiang, Peoples R China

来源：

ADVANCES IN WIRELESS SENSOR NETWORKS | 2015年 / 501卷

关键词：

Activity recognition; Random forests; Mobile sensors; Real time; ACTIVITY RECOGNITION;

D O I：

10.1007/978-3-662-46981-1_60

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

To overcome the defects that previous research cannot recognize human activities accurately in real-time, we proposed a novel method, which collects data from the accelerator and gyroscope on a mobile phone, and then extracts features of both time domain and frequency domain. These features are used to learn random forest models offline, which make our mobile app can recognize human activities accurately online in real-time. Verified by theoretical analysis and a large number of contrast experiments, the recognition is rapid and accurate on mobile phones with accuracy at 97 %.

引用

页码：638 / 650

页数：13

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