Browse > Article
http://dx.doi.org/10.21288/resko.2016.10.4.329

Real-time Sign Language Recognition Using an Armband with EMG and IMU Sensors  

Kim, Seongjung (연세대학교 보건과학대학 의공학부)
Lee, Hansoo (연세대학교 보건과학대학 의공학부)
Kim, Jongman (연세대학교 의공학과)
Ahn, Soonjae (연세대학교 의공학과)
Kim, Youngho (연세대학교 의공학과)
Publication Information
Journal of rehabilitation welfare engineering & assistive technology / v.10, no.4, 2016 , pp. 329-336 More about this Journal
Abstract
Deaf people using sign language are experiencing social inequalities and financial losses due to communication restrictions. In this paper, real-time pattern recognition algorithm was applied to distinguish American Sign Language using an armband sensor(8-channel EMG sensors and one IMU) to enable communication between the deaf and the hearing people. The validation test was carried out with 11 people. Learning pattern classifier was established by gradually increasing the number of training database. Results showed that the recognition accuracy was over 97% with 20 training samples and over 99% with 30 training samples. The present study shows that sign language recognition using armband sensor is more convenient and well-performed.
Keywords
Sign Language Recognition; Armband sensor; sEMG; Inertial sensor;
Citations & Related Records
연도 인용수 순위
  • Reference
1 J. Singha, K. Das, "Recognition of Indian Sign Language in Live Video," International Journal of Computer Applications, vol 70, no 19, pp. 17-22, 2013.   DOI
2 F. Camastra, D.D. Felice, "LVQ-based Hand Gesture Recognition using a Data Glove," In Proc. The 22th Italian Workshop on Neural Networks, Salerno, Italy, pp. 159-168. 2012
3 C. Oz, M.C. Leu, "American Sign Language word recognition with a sensory glove using artificial neural networks," Engineering Applications of Artificial Intelligence, vol 24, no 7, pp. 1204-1213, 2011.   DOI
4 X. Zhang, X. Chen, Y. Li, V. Lantz, K. Wang, J. Yang, "A Framework for Hand Gesture Recognition Based on Accelerometer and EMG Sensors," IEEE transactions on systems, Man and cybernetics-Part A: Systems and Humans, vol 41, no 6, pp. 1064-1076, 2011.   DOI
5 J. Wu, Z. Tian, L. Sun, L. Estevez, R. Jafari, "Real-time American Sign Language Recognition Using Wrist-worn Motion and Surface EMG Sensors," In Proc. The IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks(BSN), Cambridge, MA, USA, pp. 1-6. 2015.
6 H.T. J, "Trend of gesture recognition technology using wearable device," The Institute of Electronics and Information Engineers, vol. 42, no. 42, pp. 56-62, 2015.
7 S. Lake, M. Bailey, A. Grant, "Method and apparatus for analyzing capative EMG and IMU sensor signals for gesture control," U.S. Patent No. 9,299,248, 2016.
8 S. Solnik, P. Devita, P. Rider, B. Long, T. Hortobagyi, "Teager-Kaiser Operator improves the accuracy of EMG onset detection independent of signal-to-noise ratio," Acta of Bioengineering and Biomechanics, vol 10, no 2, pp. 65-68, 2008.
9 H. Demuth, M. Beale, M. Hagan, Neural Network Toolbox for use with Matlab, Natick, MA, USA, version 3 Mathworks, pp. 128-132, 1998.
10 J.S. Kim, W. Jang, Z. Bien, "A dynamic gesture recognition system for the Korean sign language(KSL)," IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), vol. 26, no. 2, pp. 354-359, 1996.   DOI
11 C. Manresa, J. Varona, R. Mas, F.J. Perales, "Hand tracking and gesture recognition for human-computer interaction," ELCVIA Electronic letters on computer vision and image analysis, vol. 5, no. 3, pp. 96-104, 2005.   DOI
12 World Health Organization, Multi-country assessment of national capacity to provide hearing care, Switzerland, Geneva, WHO Documents & publications, pp. 10-13, 2013.
13 S.C.W. Ong, S. Ranganath, "Automatic sign language analysis: A survey and the future beyond lexical meaning," IEEE transactions on pattern analysis and machine intelligence, vol. 27, no. 6, pp.873-891, 2005.   DOI
14 D. Barberis, N. Garazzino, P. Prinetto, G. Tiotto, A Savino, U. Shoaib, N. Ahmad, "Language resources for computer assisted translation from italian to italian sign language of deaf people," in Proc. Accessibility Reaching Everywhere AEGIS Workshop and International Conference, Brussels, Belgium, pp.96-104. 2011
15 C. Dong, M.C. Leu, Z. Yin, "American Sign Language Alphabet Recognition Using Microsoft Kinet," in Proc. The IEEE Conference on Computer Vision and Pattern Recognition Workshop, Boston, MA, USA, pp. 44-52. Jun. 2015