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IEICE TRANSACTIONS on Information

Activity Recognition Based on an Accelerometer in a Smartphone Using an FFT-Based New Feature and Fusion Methods

Yang XUE, Yaoquan HU, Lianwen JIN

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Summary :

With the development of personal electronic equipment, the use of a smartphone with a tri-axial accelerometer to detect human physical activity is becoming popular. In this paper, we propose a new feature based on FFT for activity recognition from tri-axial acceleration signals. To improve the classification performance, two fusion methods, minimal distance optimization (MDO) and variance contribution ranking (VCR), are proposed. The new proposed feature achieves a recognition rate of 92.41%, which outperforms six traditional time- or frequency-domain features. Furthermore, the proposed fusion methods effectively improve the recognition rates. In particular, the average accuracy based on class fusion VCR (CFVCR) is 97.01%, which results in an improvement in accuracy of 4.14% compared with the results without any fusion. Experiments confirm the effectiveness of the new proposed feature and fusion methods.

Publication
IEICE TRANSACTIONS on Information Vol.E97-D No.8 pp.2182-2186
Publication Date
2014/08/01
Publicized
Online ISSN
1745-1361
DOI
10.1587/transinf.E97.D.2182
Type of Manuscript
LETTER
Category
Human-computer Interaction

Authors

Yang XUE
  South China University of Technology
Yaoquan HU
  South China University of Technology
Lianwen JIN
  South China University of Technology

Keyword