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Real-Time Recognition of Human Daily Motion with Smartphone Sensor

Volume 14, Number 4, April 2018, pp. 593-602
DOI: 10.23940/ijpe.18.04.p1.593602

Qishou Xiaa,b, Xiaoling Yina,b,*, Juan Hea, and Feng Chena

aSchool of Information Science and Technology, Northwest University, Xi′an, 710127, China

bCollege of Mathematics and Computer Science, Chizhou University, Chizhou, 247000, China

(Submitted on January 21, 2018; Revised on February 15, 2018; Accepted on March 21, 2018)

Abstract:

Aiming at problems regarding the recognition of motion states by existing smartphones, such as poor real-time performance, less movement category and complex algorithm, this paper proposes a method of using smartphone sensors to recognize six kinds of real time human movement states. Firstly, daily human movement data is acquired through smartphone acceleration sensors and gravitational acceleration sensors, and original data is handled with correction, smoothing, segmentation and direction-independent processing. Secondly, the footsteps identification algorithm is used to calculate peaks and troughs of footsteps from which the time-domain feature vectors are extracted. Finally, the movement states are classified according to feature vectors, and the Hierarchical Support Machines (H-SVMs) is used to recognize daily movement states. Experimental results show this method can effectively reduce the computational load of smartphones and improve real-time performance and accuracy of movement states recognition. This method is suitable for other similar behavior recognitions.

 

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