Browse > Article
http://dx.doi.org/10.9717/kmms.2017.20.11.1842

A Study on MYO-based Motion Estimation System Design for Robot Control  

Chae, Jeongsook (Dept. of Multimedia Eng., Graduate School, Dongguk University-Seoul)
Cho, Kyungeun (Dept. of Multimedia Eng., Graduate School, Dongguk University-Seoul)
Publication Information
Journal of Korea Multimedia Society / v.20, no.11, 2017 , pp. 1842-1848 More about this Journal
Abstract
Recently, user motion estimation methods using various wearable devices have been actively studied. In this paper, we propose a motion estimation system using Myo, which is one of the wearable devices, using two Myo and their dependency relations. The estimated motion is used as a command for remotely controlling the robot. Myo's Orientation and EMG signals are used for motion estimation. These two type data sets are used complementarily to increase the accuracy of motion estimation. We design and implement the system according to the proposed method and analyze the results through experiments. As a result of comparison with previous studies, the accuracy of motion estimation can be improved by about 12.3%.
Keywords
Motion Estimation; Bayesian Probability; Myo; Orientation; EMG;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
연도 인용수 순위
1 G.C. Luh, H.A. Lin, Y.H. Ma, and C.J. Yen, "Intuitive Muscle-Gesture based Robot Navigation Control Using Wearable Gesture Armband," Proceedings of the 2015 International Conference on Machine Learning and Cybernetics, pp. 389-395, 2015.
2 S.B. Lee, and I.H. Jung, "A Design and Implementation of Natural User Interface System Using Kinect," Journal of Digital Contents Society, Vol. 15, No. 4, pp. 473-480, 2104.   DOI
3 G.C. Chang, J.W. Park, C.M. Oh, and C.W. Lee, "A Decision Tree Based Real-time Hand Gesture Recognition Method Using KINECT," Journal of Multimedia Information System, Vol. 16, No. 12, pp. 1393-1402, 2013.
4 M.G. Hseo, S.Y. Kim, J.B. Ju, and C. Lee, "Frequency Estimation of Human Movements Using Kinect and Its Application," Journal of korea Multimedia Society, Vol. 20, No. 8, pp. 1248-1257, 2017.   DOI
5 M. Khademi, H.M. Hondori, A. Mckenzie, L. Dodakian, C.V. Lopes, and S.C. Cramer, et al., "Free-Hand Interaction with Leap Motion Controller for stroke Rehabilitation," Proceeding of International Conference of Human-Computer Interaction, pp. 1663-1668, 2014.
6 S.M Glegg, S.K. Tatla, and L. Holsti, "The GestureTek Virtual Reality System in Rehabilitation: A Scoping Review," Disabil Rehab Assistive Technology, Vol. 9, No. 2, pp. 89-111, 2014.   DOI
7 I. Mendez, B.W. Hansen, C.M. Grabow, E.J.L. Smedegaard, N.B. Skogberg, and X.J. Uth, et al., "Evaluation of the Myo Armband for the Classification of Hand Motions," International Conference on Rehabilitation Robotics, pp. 1211-1214, 2017.
8 S.G. Lee, Y.S. Sung, and J.H. Park, "Motion Estimation Framework and Authoring Tools Based on MYOs and Bayesian Probability," Multimedia Tools and Applications, Vol. 76, Issue 280, pp. 1-20, 2016.
9 D. Strelow, and S. Singh, "Optimal Motion Estimation from Visual and Inertial Measurements," Proceeding of the 6th IEEE Workshop on Applications of Computer Vision, pp. 314-319, 2002.
10 D. Majoe, L. Widmer, P. Tschiemer, and J. Gutknecht, "Tai Chi Motion Recognition, Embedding the HMM Method on A Wearable," Proceeding of IEEE Joint Conferences on Pervasive Computing, pp. 339-344, 2009.
11 Korea Creative Content Agency, Culture Technology (CT) Deep Report, Vol. 12, Brain Computer Interface Technology Trend, 2011.
12 I.H. Moon, M.J. Lee, J.U. Chu, and M.S. Mun, "Wearable EMG-based HCI for Electric-Powered Wheelchair Users with Motor Disabilities," Proceeding of IEEE International Conference on Robotics and Automation, pp. 2649-2654, 2005.
13 K.W. Park, and G.Y. Hwang, "Movement Intention Detection of Human Body Based on Electromyographic Signal Analysis Using Fuzzy C-Means Clustering Algorithm," Journal of korea Multimedia Society, Vol. 19, No. 1, pp. 68-79, 2016.   DOI
14 S.I. Yang, J.H. Hong, and S.B. Cho, "Activity Recognition Based on Multi-modal Sensors Using Dynamic Bayesian Networks," Journal of Korean Institute of Information Scientistsand Engineers, Vol. 15, No. 1, pp. 72-76, 2009.
15 M. Abduo, and M. Galster, Myo Gesture Control Armband for Medical Applications, University of Canterbury, Engineering Reports, 2015.