Application of the Device of Measurement of Bioelectric Activity of Muscles and Nerve Structures for Gesture Recognition - Application of Gesture Recognition on the Example of Action Game

Levanov Alexey Alexendrovich


Using the device bioPlux we can identify a set of user gestures, and based on them to create a multimodal interface. Gestures are selected so that they are not dependent on each other. The main goal of the research is to create a simple game which can be controlled by using different hand movements. Relevance of the topic from the practical point of view is determined by the need to create a software system that can use sign language interface in real time.


  1. Alon, J., Athistos, V., Yuan, Q., and Sclaroff S. (2005). Simultaneous Localization and Recognition of Dynamic Hand Gestures. Proc. of WACV MOTION'05, 2, 254-260.
  2. Avilts-Aniaga, H., H., Sucart, L., E., and Mendozaz, C., E. (2003). Visual Gesture Recognitions Using Dynamic Naive Bayesian Classifiers. Proc. of IEEE Internat. Workshop on Robot and Human Interac. Com.- Milibrae, 133-138.
  3. Bobick, A. F., & Wilson, A. D. (1997). A State-Based Approach to the Representation and Recognition of Gesture. Proc. of IEEE Transactions on pattern analysis and machine intelligence, 19(12), 1325-1337.
  4. Brown, L., G. (1992). A Survey of Image Registration Techniques. Computing Surveys, 24(4), 325-376.
  5. Umpire Gesture Recognition. Structural, Syntactic, and Stat. Pat. Recog., Vol. 3138, pp. 859-867.
  6. Cutler, R., & Turk, M. (1998). View-based Interpretation of Real-time Optical Flow for Gesture Recognition. Proc. of Third IEEE Intern. Conf. on Autom. Face and Gesture Recog. Nara, 416-421.
  7. Darwiche, A. A. (2001). Differential Approach to Inference in Bayesian Networks. Journal of the ACM, 50(3), 280 -305.
  8. Davis, J.W., & Shah, M. (1992). Gesture Recognition. Proc. of European Conf. Comp. Vis., 331-340.
  9. Devyatkov, V. & Alfimtsev, A. (2008). Optimal Fuzzy Aggregation of Secondary Attributes in Recognition Problems. Proc. of 16-th International Conference in Central Europe on Computer Graphics. Visualization and Computer Vision. Plzen, 78-85.
  10. Devyatkov, V., & Alfimtsev, A. (2009). Dynamic Gesture Recognition Using Fuzzy Model. Proc. of the 13th World Multi-Conference on Systemics, Cybernetics and Informatics (WMSCI 2009).-Orlando, USA.- p. 145-150.
  11. Ekman,P., & Friesen,W. (1969). The Repertoire of Nonverbal Behavior: Categories, Origins, Usage and Coding. Semiotica, 1, 49-98.
  12. Freeman, W.T., Tanaka, K., Ohta, J., Kyuma K. (1996). Computer Vision for Computer Games. In Proc. IEEE Int. Conf. on Face & Gesture Recognition, 100-105.
  13. Frei, W., & Chen, C. C. (1977). Fast Boundary Detection: A Generalization and New Approach. IEEE Trans. Comput., 26(10), 988-998.
  14. Garcia, C., & Tziritas, G. (1999). Face Detection Using Quantized Skin Color Regions Merging and Wavelet Packet Analysis. IEEE Transactions on multimedia, 1(3), 264-277.
  15. Gould, K., & Shah, M. (1989). The Trajectory Primal Sketch: A MultiScale Scheme for Representing Motion Characteristics. Proc. of Comp. Vis. and Pattern Rec, 79-85.
  16. Schunck, B. (1998). Determining Optical Flow. Artificial Intelligence, 17, 185-203.
  17. Johansson, G. (1964) Perception of Motion and Changing Form. Scandanavian J. Psychology, 5, 181-208.
  18. Kang, H., Lee, C. W., Jung, K. (2004) Recognition-Based Gesture Spotting in Video Games. Pattern Rec. Let., V. 25, I. 15, 1701-1714.
  19. Keir, P. Elgoyhen, J. Naef, M. Payne, J. Horner, M. Anderson, P. (2006). Gesture-Recognition with Nonreferenced Tracking. 1st IEEE Symposium on 3D User interfaces, 137.
  20. Kirsch, R. (1977). Computer Determination of the Constituent Structure of Biological Images. Comput. Biomed, 4(3), 315-328.
  21. Kim, N., An, Y., Cha B. (2009). Gesture Recognition Based on Neural Networks for Dance Game Contents. International Conference on New Trends in Information and Service Science, 134-1139.
  22. Kwak, K., & Pedrycz, W. (2005). Face Recognition: A Study in Information Fusion Using Fuzzy Integral. Patt. Recog. Lett, 26, 719-733.
  23. Kyle, J. & Woll B. (1988) Sign Language: The Study of deaf People and Their Language. Cambridge University Press, 328 p.
  24. Lienhart, R., & Maydt J. (2002). An Extended Set of Haar-like Features for Rapid Object Detection. IEEE ICIP, 1, 900-903.
  25. Lienhart, R., Kuranov, A., Pisarevsky, V. (2003). Empirical Analysis of Detection Cascades of Boosted Classifiers for Rapid Object Detection. Proc. of DAGM03, 297-304.
  26. Liu, Z. (2001). Dynamic Image Sequence Analysis Using Fuzzy Measures. IEEE trans. on sys., man, and cybern, 31(4), 557-572.
  27. Mamiya, H., Sato, T., Fukuchi, K., Koike, H. (2007) A Tabletop Entertainment System and Finger Tapping Gesture Recognition. In Proceedings of WISS, JSSST, 53-58.
  28. Miklós, I., & Meyer, I. (2005) A Linear Memory Approach for Baum-Welch Training. BMC Bioinformatics, 6(231), 1471-2105.
  29. Nishikawa, A., Hosoi, T., Koara, K., Negoro, D., Hikita, A., Asano, S., Kakutani, H., Miyazaki, F., Sekimoto, M., Yasui, M., Miyake, Y., Takiguchi, S., and Monden, M. (2003). FAce MOUSe: A Novel HumanMachine Interface for Controlling the Position of a Laparoscope. IEEE Trans on Robotics and Automation 19:5:825-841.
  30. Ong, S., & Ranganath, S. (2005). Automatic Sign Language Analysis: A Survey and the Future beyond Lexical Meaning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 5(6), 873-891.
  31. Park, J. Y., Yi, J. H. (2008). Gesture Recognition Based Interactive Boxing Game. Scientific Literature Digital Library:
  32. Patel, S. (1995). A lower-complexity Viterbi approach. Acoustics, Speech, and Signal Processing, 1, 592-595.
  33. Rabiner, L., & Juang, B.H. (1993). Fundamentals of Speech Recognition. Prentice Hall.
  34. Rett, J., & Dias, J. (2006). Gesture Recognition Using a Marionette Model and Dynamic Bayesian Networks. Lecture notes in computer science, 4142, 69-80.
  35. Rigoll, G., Kosmala, A., and Eickeler, S. (1997). High Performance Real-Time Gesture Recognition Using Hidden Markov Models. Proc. of the Internat. Gesture Workshop on Gesture and Sign Lang. in HumanComputer Interac, 69-80.
  36. Russel, S.J., & Norvig, P. (2002). Artificial Intelligence. A modern approach. Upper Saddle River/new Jersey, Prentice Hall.
  37. Sandberg, A. (1997). Gesture Recognition using Neural Networks. Master thesis. Stockholm.
  38. Schultz M, Gill J, Zubairi S, Huber R, Gordin F (2003) Bacterial Contamination of Computer Keyboards in a Teaching Hospital. Infect Control Hosp Epidemiol 24:302-303
  39. Schumeyer, R. P., & Barner, K. E. (1998). A Color-Based Classifier for Region Identification in Video. SPIE Visual Communications Image Processing, 3309, 189- 200.
  40. Shapiro, L.G., & Stockman, G.S. (2001). Computer Vision. Upper Saddle River, N.J., Prentice-Hall.
  41. Sharma, R. (2003). Speech-Gesture Driven Multimodal Interfaces for Crisis Management. Proc. of the IEEE, 91, 1327-1354.
  42. Sigal, L., & Sclaroff S. (2004). Skin Color-Based Video Segmentation under Time-Varying Illumination. IEEE Transactions on pattern analysis and machine intelligence, 26(7), 862-877.
  43. Silva, M., Courboulay, V., Prigent, A., Estraillier, P. (2008). Real-Time Face Tracking for Attention Aware Adaptive Games. ICVS 2008, 99-108.
  44. Starner, T., Weaver, J., and Pentland, A. (1998). RealTime American Sign Language Recognition Using Desk and Wearable Computer Based Video. IEEE Trans. Pattern Analysis and Machine Intelligence, 20(12), 1371-1375.
  45. Song, P., Yu, H., Winkler, S. (2009). Vision-based 3D Finger Interactions for Mixed Reality Games with Physics Simulation. The International Journal of Virtual Reality, 8(2):1-6.
  46. Su, J., & Zhang, H. (2005). Full Bayesian Network Classifiers. Proc. of the 23rd international conference on Machine learning, 897 - 904.
  47. Tahani, H., & Keller, J. M. (1990). Information Fusion in Computer Vision Using the Fuzzy Integral. IEEE transactions on systems, man, and cybernetics, 20(3), 733-741.
  48. Tomasi, C., Petrov, S., and Sastry, A. (2003). 3D Tracking = Classification + Interpolation. Proc. of Int. Conf. Computer Vision, 1441-1448.
  49. Viola, P., & Jones, M. (2001) Rapid Object Detection using a Boosted Cascade of Simple Features. IEEE CVPR, 1, 511-518.
  50. Winkler, S., Yu, H., Zhou, Z.Y. (2007). Tangible Mixed Reality Desktop for Digital Media Management. In SPIE Engineering Reality of Virtual Reality, Vol. 6490B.
  51. Winstone, P.G. (1992). Artificial Intelligence. Reading/Massachusetts, Addison-Wesley Publishing Company.
  52. Wong, S.F., & Cipolla, R. (2006). Continuous Gesture Recognition using a Sparse Bayesian Classifier. Proc of 18th Internat. Conf. on Pattern Recognition, 1084- 1087.
  53. Wu, H., Chen, Q., and Yachida, M. (1999). Face Detection From Color Images Using a Fuzzy Pattern Matching Method. IEEE Transactions on pattern analysis and machines intelligence, 21(6), 557-563.
  54. Yamato, J., Ohya, J., and Ishii, K. (1992). Recognizing Human Action in Time-Sequential Images Using Hidden Markov Model. Proc. of Comp. Vis. and Pattern Rec, 379-385.
  55. Waibel, A. (1996) A Real-Time Face Tracker. Proc. of the Third IEEE Workshop on Applicat. of Comp. Vision, 142-147.
  56. Ye, G., Corso, J., Hager, G., (2004). Gesture Recognition Using 3D Appearance and Motion Features. Proc. of Workshop on Real-time Vision for Human-Computer Interaction, 160-161.
  57. Zeng, T., J., Wang, Y., Freedman, M., T., and Mun, S., K. (1997). Finger Tracking for Breast Palpation Quantification Using Color Image Features. SPIE Optical Eng., 36(12), 3455-3461

Paper Citation

in Harvard Style

Alexendrovich L. (2013). Application of the Device of Measurement of Bioelectric Activity of Muscles and Nerve Structures for Gesture Recognition - Application of Gesture Recognition on the Example of Action Game . In Proceedings of the International Conference on Biomedical Electronics and Devices - Volume 1: MHGInterf, (BIOSTEC 2013) ISBN 978-989-8565-34-1, pages 301-305. DOI: 10.5220/0004365803010305

in Bibtex Style

author={Levanov Alexey Alexendrovich},
title={Application of the Device of Measurement of Bioelectric Activity of Muscles and Nerve Structures for Gesture Recognition - Application of Gesture Recognition on the Example of Action Game},
booktitle={Proceedings of the International Conference on Biomedical Electronics and Devices - Volume 1: MHGInterf, (BIOSTEC 2013)},

in EndNote Style

JO - Proceedings of the International Conference on Biomedical Electronics and Devices - Volume 1: MHGInterf, (BIOSTEC 2013)
TI - Application of the Device of Measurement of Bioelectric Activity of Muscles and Nerve Structures for Gesture Recognition - Application of Gesture Recognition on the Example of Action Game
SN - 978-989-8565-34-1
AU - Alexendrovich L.
PY - 2013
SP - 301
EP - 305
DO - 10.5220/0004365803010305