Gesture control technology based on surface

HU Xin-hui; LI Wen-bin; KAN Jiang-ming

doi:10.13332/j.1000-1522.20160290

Journal of Beijing Forestry University > 2017 > 39(2): 117-124. > DOI: 10.13332/j.1000-1522.20160290

HU Xin-hui, LI Wen-bin, KAN Jiang-ming. Gesture control technology based on surface[J]. Journal of Beijing Forestry University, 2017, 39(2): 117-124. DOI: 10.13332/j.1000-1522.20160290

Citation:

HU Xin-hui, LI Wen-bin, KAN Jiang-ming. Gesture control technology based on surface[J]. Journal of Beijing Forestry University, 2017, 39(2): 117-124. DOI: 10.13332/j.1000-1522.20160290

Citation:

HU Xin-hui, LI Wen-bin, KAN Jiang-ming. Gesture control technology based on surface[J]. Journal of Beijing Forestry University, 2017, 39(2): 117-124. DOI: 10.13332/j.1000-1522.20160290

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Gesture control technology based on surface

School of Technology, Beijing Forestry University, Beijing, 100083, P

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Received Date: September 06, 2016
Revised Date: October 04, 2016
Published Date: January 31, 2017

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Abstract

Abstract

To realize the gesture control for automatic pruning machine needs to complete two parts of work, gesture recognition and wireless remote control and therefore, the technology of hand gesture recognition based on the surface electromyography (sEMG) signal was studied. Firstly, according to six working states of pruning machine, six corresponding gestures were defined, which comprised fist, palm lateral supination, palm lateral pronation, palm supination, palm pronation and finger spread. Secondly, collected sEMG signal was preprocessed, including noise elimination and motion segmentation. The motion signals were then analyzed in time domain and time-frequency domain, and three kinds of features were computed, which were mean absolute value, coefficient of autoregressive parameter model, and average energy of each subband after wavelet decomposition. Lastly, support vector machine classifier was constructed to conduct single-user and multi-user recognition experiments and the best parameters of the classifier were obtained by V-fold cross validation. The results of gesture recognition experiments showed that, the highest accuracy of single-user experiment reached 100% and the average accuracy of single-user experiment (98.07%) was higher than that of multi-user experiment (91.19%). This study lays foundation for the implementation of the follow-up work of gesture control for pruning machine and provides an effective new way to promote the intelligent and human-computer interaction process of forestry machinery.
- surface electromyography,
- gesture recognition,
- pruning machine,
- gesture control

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References (24)

References

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