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DOI: 10.14569/IJACSA.2012.030101
PDF

Analysis and Selection of Features for Gesture Recognition Based on a Micro Wearable Device

Author 1: Yinghui Zhou
Author 2: Lei Jing
Author 3: Junbo Wang
Author 4: Zixue Cheng

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 3 Issue 1, 2012.

  • Abstract and Keywords
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Abstract: More and More researchers concerned about designing a health supporting system for elders that is light weight, no disturbing to user, and low computing complexity. In the paper, we introduced a micro wearable device based on a tri-axis accelerometer, which can detect acceleration change of human body based on the position of the device being set. Considering the flexibility of human finger, we put it on a finger to detect the finger gestures. 12 kinds of one-stroke finger gestures are defined according to the sensing characteristic of the accelerometer. Feature is a paramount factor in the recognition task. In the paper, gestures features both in time domain and frequency domain are described since features decide the recognition accuracy directly. Feature generation method and selection process is analyzed in detail to get the optimal feature subset from the candidate feature set. Experiment results indicate the feature subset can get satisfactory classification results of 90.08% accuracy using 12 features considering the recognition accuracy and dimension of feature set.

Keywords: Internet of Things; Wearable Computing; Gesture Recognition; Feature analysis and selection; Accelerometer.

Yinghui Zhou, Lei Jing, Junbo Wang and Zixue Cheng, “Analysis and Selection of Features for Gesture Recognition Based on a Micro Wearable Device” International Journal of Advanced Computer Science and Applications(IJACSA), 3(1), 2012. http://dx.doi.org/10.14569/IJACSA.2012.030101

@article{Zhou2012,
title = {Analysis and Selection of Features for Gesture Recognition Based on a Micro Wearable Device},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2012.030101},
url = {http://dx.doi.org/10.14569/IJACSA.2012.030101},
year = {2012},
publisher = {The Science and Information Organization},
volume = {3},
number = {1},
author = {Yinghui Zhou and Lei Jing and Junbo Wang and Zixue Cheng}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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