DocumentCode :
714170
Title :
An adaptive time window method for human activity recognition
Author :
Zhang Sheng ; Chen Hailong ; Jiang Chuan ; Zhang Shaojun
Author_Institution :
Key Lab. of Adv. Sensor & Integrated Syst., Tsinghua Univ., Shenzhen, China
fYear :
2015
fDate :
3-6 May 2015
Firstpage :
1188
Lastpage :
1192
Abstract :
This paper studies the problem of human activity recognition. Traditionally, the data collected by the accelerometer is preprocessed with a fixed time window, and features for human activity recognition model are extracted in this framework. However, some human activities are quasi-periodic, which means that classification accuracy can be improved if adaptive time window is adopted instead. As human activities can be divided into periodic and non-periodic class, in order to extract features more accurately for the classification, the adaptive time window is then designed specifically to cope with the two categories. Finally, experiment is conducted to show that the adaptive time window method improves the classification accuracy in the identification of six kinds of activities including sitting, walking, running, etc., compared with previous fixed time window method.
Keywords :
accelerometers; feature extraction; gait analysis; sensors; signal processing; accelerometer; adaptive time window method; feature extraction; fixed time window; human activity recognition model; quasiperiodic human activities; Acceleration; Computational efficiency; Correlation; Data mining; Decision trees; Feature extraction; Sensors;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Electrical and Computer Engineering (CCECE), 2015 IEEE 28th Canadian Conference on
Conference_Location :
Halifax, NS
ISSN :
0840-7789
Print_ISBN :
978-1-4799-5827-6
Type :
conf
DOI :
10.1109/CCECE.2015.7129445
Filename :
7129445
Link To Document :
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