DocumentCode :
2791284
Title :
Activity recognition from acceleration data using AR model representation and SVM
Author :
He, Zhen-yu ; Jin, Lian-wen
Author_Institution :
Sch. of Electron. & Inf. Eng., South China Univ. of Technol., Guangzhou
Volume :
4
fYear :
2008
fDate :
12-15 July 2008
Firstpage :
2245
Lastpage :
2250
Abstract :
In this paper, the autoregressive (AR) model of time-series is presented to recognize human activity from a tri-axial accelerometer data. Four orders of autoregressive model for accelerometer data is built and the AR coefficients are extracted as features for activity recognition. Classification of the human activities is performed with support vector machine (SVM). The average recognition results for four activities (running, still, jumping and walking) using the proposed AR-based features are 92.25%, which are better than using traditional frequently used time domains features (mean, standard deviation, energy and correlation of acceleration data) and FFT features. The results show that AR coefficients obvious discriminate different human activities and it can be extract as an effective feature for the recognition of accelerometer date.
Keywords :
autoregressive processes; feature extraction; image classification; support vector machines; time series; acceleration data; autoregressive model; feature extraction; human activity classification; human activity recognition; support vector machine; time series; Acceleration; Accelerometers; Data mining; Feature extraction; Humans; Legged locomotion; Machine learning; Pattern recognition; Support vector machine classification; Support vector machines; Activity recognition; Autoregressive model; Feature extraction; SVM; Tri-axial accelerometer data;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning and Cybernetics, 2008 International Conference on
Conference_Location :
Kunming
Print_ISBN :
978-1-4244-2095-7
Electronic_ISBN :
978-1-4244-2096-4
Type :
conf
DOI :
10.1109/ICMLC.2008.4620779
Filename :
4620779
Link To Document :
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