Abstract
Aimed at the problem of variety and accuracy of gesture recognition in human-robot interaction, this paper proposes a gesture recognition method based on DS evidence theory for vision and electromyography information fusion. Through the feature analysis of visual images and electromyography signals, human gestures are correctly recognized. The histogram of oriented gradient features of the visual images and the time-domain features of the electromyography signals are extracted respectively to describe the gestures information. Support vector machine is used as classification algorithm. The two description methods are fused at the decision-level by DS evidence theory, which significantly improves the accuracy of gesture recognition. Experiments demonstrate the proposed method has an average accuracy of 93.8% for 36 gestures.
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References
Hasan, H.S., Kareem, S.A.: Human computer interaction for vision based hand gesture recognition: a survey. In: 2012 International Conference on Advanced Computer Science Applications and Technologies (ACSAT), pp. 55–60 (2012)
Cheng, H., Yang, L., Liu, Z.: Survey on 3d hand gesture recognition. IEEE Trans. Circuits Syst. Video Technol. 26(9), 1659–1673 (2016)
Saha, S., Lahiri, R., Konar, A., Banerjee, B., Nagar, A.K.: Hmm-based gesture recognition system using kinect sensor for improvised human-computer interaction. In: 2017 International Joint Conference on Neural Networks (IJCNN), pp. 2776–2783 (2017)
Kläser, A., Marszałek, M., Schmid, C., Zisserman, A.: Human focused action localization in video. In: Kutulakos, K.N. (ed.) ECCV 2010. LNCS, vol. 6553, pp. 219–233. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-35749-7_17
Al-Asadi, E., Fatimah, K., Sulaiman, P., Yaakob, R.: Fast marching method and modified features fusion in enhanced dynamic hand gesture segmentation and detection method under complicated background. J. Ambient Intell. Hum. Comput. 9(3), 755–769 (2018)
Naik, G.R., Al-Timemy, A.H., Nguyen, H.T.: Transradial amputee gesture classification using an optimal number of sEMG sensors: an approach using ICA clustering. IEEE Trans. Neural Syst. Rehabil. Eng. 24(8), 837–846 (2016)
Menon, R., Di Caterina, G., Lakany, H., Petropoulakis, L., Conway, B.A., Soraghan, J.J.: Study on interaction between temporal and spatial information in classification of EMG signals for myoelectric prostheses. IEEE Trans. Neural Syst. Rehabil. Eng. 25(10), 1832–1842 (2017)
Naik, G., Kumar, D., Arjunan, S.: Towards classification of low-level finger movements using forearm muscle activation: a comparative study based on ICA and fractal theory. Int. J. Biomed. Eng. Technol. 6, 150–162 (2011)
Lu, Z., Xiang, C., Li, Q., Zhang, X., Zhou, P.: A hand gesture recognition framework and wearable gesture-based interaction prototype for mobile devices. Hum. Mach. Syst. IEEE Trans. 44, 293–299 (2014)
Kuang, H., Chen, L., Gu, F., Chen, J., Chan, L.L.H., Yan, H.: Combining region-of-interest extraction and image enhancement for nighttime vehicle detection. IEEE Intell. Syst. 31, 1 (2016)
Acknowledgements
This work is partially supported by the National Natural Science Foundation of China (61773351), and the Program for Science & Technology Innovation Talents in Universities of Henan Province (20HASTIT031).
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Dong, M., Peng, J., Ding, S., Wang, Z. (2022). Vision and EMG Information Fusion Based on DS Evidence Theory for Gesture Recognition. In: Deng, Z. (eds) Proceedings of 2021 Chinese Intelligent Automation Conference. Lecture Notes in Electrical Engineering, vol 801. Springer, Singapore. https://doi.org/10.1007/978-981-16-6372-7_55
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DOI: https://doi.org/10.1007/978-981-16-6372-7_55
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