Abstract
Computers are emerging as the most utilitarian products in the human society and therefore the interaction between humans and computers will have a very significant influence in the society. As a result, enormous amount of efforts are being made to augment the research in the domain of human computer interaction to develop more efficient and effective techniques for the purpose of reducing the barrier of humans and computers. The primary objective is to develop a conducive environment in which there is feasibility of very natural interaction between humans and computers. In order to achieve this goal, gestures play a very pivotal role and are the core area of research in this domain. Hand gesture recognition is a significant component of virtual Reality finds applications in numerous fields including video games, cinema, robotics, education, marketing, etc. Virtual reality also caters to a variety of healthcare applications involving the procedures used in surgical operations including remote surgery, augmented surgery, software emulation of the surgeries prior to actual surgeries, therapies, training in the medical education, medical data visualization and much more. A lot of tools and techniques have. Been developed to cater to the development of the such virtual environments. Gesture recognition signifies the method of keeping track of gestures of humans, to representing and converting the gestures to meaningful signals. Contact based and vision based devices are used for creating and implementing these systems of recognition effectively. The chapter begins with the introduction of hand gesture recognition and the process of carrying out hand gesture recognition. Further, the latest research which is being in carried out in the domain of hand gesture recognition is described. It is followed by the details of applications of virtual reality and hand gesture recognition in the field of healthcare. Then, various techniques which are applied in hand gesture recognition are described. Finally, the challenges in the field of hand gesture recognition have been explained.
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References
Sinha, G., Shahi, R., & Shankar, M. (2010). Human Computer Interaction. 2010 3rd International Conference on Emerging Trends in Engineering and Technology.
Chakraborty, B. K., Sarma, D., Bhuyan, M. K., & MacDorman, K. F. (2018). Review of constraints on vision-based gesture recognition for human–computer interaction. IET Computer Vision, 12(1), 3–15.
Jaimes, A., & Sebe, N. (2007). Multimodal human-computer interaction: A survey. Computer Vision and Image Understanding, 108(1–2), 116–134.
Chapanis, A. (1965). Man machine engineering. Belmont: Wadsworth.
Norman, D. (1986). Cognitive Engineering. In D. Norman & S. Draper (Eds.), User centered design: New perspective on human-computer interaction. Hillsdale: Lawrence Erlbaum.
Picard, R. W. (1997). Affective computing. Cambridge: MIT Press.
Han, Y. (2010). A low-cost visual motion data glove as an input device to interpret human hand gestures. IEEE Transactions on Consumer Electronics, 56(2), 501–509.
Choudhury, A., Talukdar, A. K., & Sarma, K. K. (2014). A Conditional Random Field Based Indian Sign Language Recognition System under Complex Background. 2014 Fourth International Conference on Communication Systems and Network Technologies.
Habili, N., Lim, C. C., & Moini, A. (2004). Segmentation of the face and hands in sign language video sequences using color and motion cues. IEEE Transactions on Circuits and Systems for Video Technology, 14(8), 1086–1097.
Iqbal, J., Ul Haq, A., & Wali, S. (2015). Moving target detection and tracking.
Choudhury, A., Talukdar, A., Sarma, K. (2014). A novel hand segmentation method for multiple-hand gesture recognition system under complex background. In 2014 International Conference on Signal Processing and Integrated Networks, SPIN 2014. https://doi.org/10.1109/spin.2014.6776936.
Stergiopoulou, E., & Papamarkos, N. (2009). Hand gesture recognition using a neural network shape fitting technique. Engineering Applications of Artificial Intelligence, 22(8), 1141–1158.
Malima, A., Ozgur, E., & Cetin, M. (n.d.). A fast algorithm for vision-based hand gesture recognition for robot control. In 2006 IEEE 14th Signal Processing and Communications Applications. https://doi.org/10.1109/siu.2006.1659822.
Hasan, M. M., & Mishra, P. K. (2011). HSV brightness factor matching for gesture recognition system. International Journal of Image Processing (IJIP), 4(5), 456–467.
Chang, C. C., Chen, J. J., Tai, W., & Han, C. C. (2006). New approach for static gesture recognition. Journal of Information Science and Engineering, 22, 1047–1057.
Just, A. (2006). Two-handed gestures for human–computer interaction. Ph.D. thesis.
Parvini, F., & Shahabi, C. (2007). An algorithmic approach for static and dynamic gesture recognition utilising mechanical and biomechanical characteristics. International Journal of Bioinformatics Research and Applications, 3(1), 4.
Dardas, N. H., & Georganas, N. D. (2011). Real-time hand gesture detection and recognition using bag-of-features and support vector machine techniques. IEEE Transactions on Instrumentation and Measurement, 60(11), 3592–3607.
Nagi, J., Ducatelle, F., Di Caro, G. A., Ciresan, D., Meier, U., Giusti, A., et al. (2011). Max-pooling convolutional neural networks for vision-based hand gesture recognition. In 2011 IEEE International Conference on Signal and Image Processing Applications (ICSIPA).
Panwar, M. (2012). Hand gesture recognition based on shape parameters. In 2012 International Conference on Computing, Communication and Applications.
Ohn-Bar, E., & Trivedi, M. M. (2014). Hand gesture recognition in real time for automotive interfaces: A multimodal vision-based approach and evaluations. IEEE Transactions on Intelligent Transportation System, 15(6), 2368–2377.
Suk, H.-I., Sin, B.-K., & Lee, S.-W. (2010). Hand gesture recognition based on dynamic Bayesian network framework. Pattern Recognition, 43(9), 3059–3072.
Shen, X., Hua, G., Williams, L., & Wu, Y. (2012). Dynamic hand gesture recognition: An exemplar-based approach from motion divergence fields. Image and Vision Computing, 30(3), 227–235. https://doi.org/10.1016/j.imavis.2011.11.003.
Padam Priyal, S., & Bora, P. K. (2013). A robust static hand gesture recognition system using geometry based normalizations and Krawtchouk moments. Pattern Recognition, 46(8), 2202–2219. https://doi.org/10.1016/j.patcog.2013.01.033.
Hoffman, H., & Vu, D. (1997). Virtual reality: teaching tool of the twenty-first century? Academic Medicine: Journal of the Association of American Medical Colleges, 72(12), 1076–1081.
Gallagher, A. G., Ritter, E. M., Champion, H., Higgins, G., Fried, M. P., Moses, G., et al. (2005). Virtual reality simulation for the operating room: Proficiency-based training as a paradigm shift in surgical skills training. Annals of Surgery, 241(2), 364.
Aggarwal, R., Ward, J., Balasundaram, I., Sains, P., Athanasiou, T., & Darzi, A. (2007). Proving the effectiveness of virtual reality simulation for training in laparoscopic surgery. Annals of Surgery, 246(5), 771–779.
Satava, R. M. (1993). Virtual reality surgical simulator. Surgical Endoscopy, 7(3), 203–205.
Liu, J. Q., Fujii, R., Tateyama, T., Iwamoto, Y., & Chen, Y. W. (2017). Kinect-based gesture recognition for touchless visualization of medical images. International Journal of Computer and Electrical Engineering, 9(2), 421–429.
Krapichler, C., Haubner, M., Engelbrecht, R., & Englmeier, K. H. (1998). VR interaction techniques for medical imaging applications. Computer Methods and Programs in Biomedicine, 56(1), 65–74.
Khan, R. Z., & Ibraheem, N. A. (2012). Comparative study of hand gesture recognition system. In Proceedings of International Conference of Advanced Computer Science & Information Technology in Computer Science & Information Technology (CS & IT) (Vol. 2, No. 3, pp. 203–213).
Rautaray, S. S., & Agrawal, A. (2015). Vision based hand gesture recognition for human computer interaction: A survey. Artificial Intelligence Review, 43(1), 1–54.
Rehg, J. M., & Kanade, T. (1994, November). Digiteyes: Vision-based hand tracking for human-computer interaction. In Proceedings of the 1994 IEEE Workshop on Motion of Non-rigid and Articulated Objects, 1994 (pp. 16–22). IEEE.
Ramesh, V. (2003, October). Background modeling and subtraction of dynamic scenes. In Proceedings. Ninth IEEE International Conference on Computer Vision, 2003 (pp. 1305–1312). IEEE.
Zivkovic, Z. (2004, August). Improved adaptive Gaussian mixture model for background subtraction. In Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004 (Vol. 2, pp. 28–31). IEEE.
Schapire, R. E. (2003). The boosting approach to machine learning: An overview. In Nonlinear estimation and classification (pp. 149–171). New York, NY: Springer.
Kalman, R. E. (1960). A new approach to linear filtering and prediction problems. Journal of Basic Engineering, 82(1), 35–45.
Stenger, B., Mendonça, P. R., & Cipolla, R. (2001, September). Model-based hand tracking using an unscented Kalman filter. In BMVC (Vol. 1, pp. 63–72).
Isard, M., & Blake, A. (1996, April). Contour tracking by stochastic propagation of conditional density. In European Conference on Computer Vision (pp. 343–356). Berlin, Heidelberg: Springer.
Shan, C., Tan, T., & Wei, Y. (2007). Real-time hand tracking using a mean shift embedded particle filter. Pattern Recognition, 40(7), 1958–1970.
Stenger, B., Thayananthan, A., Torr, P. H., & Cipolla, R. (2006). Model-based hand tracking using a hierarchical bayesian filter. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(9), 1372–1384.
Nadgeri, S. M., Sawarkar, S. D., & Gawande, A. D. (2010, November). Hand gesture recognition using CAMSHIFT algorithm. In 2010 3rd International Conference on Emerging Trends in Engineering and Technology (ICETET) (pp. 37–41). IEEE.
Peng, J. C., Gu, L. Z., & Su, J. B. (2006). The hand tracking for humanoid robot using Camshift algorithm and Kalman filter. Journal-Shanghai Jiaotong University-Chinese Edition, 40(7), 1161.
Luo, Y., Li, L., Zhang, B. S., & Yang, H. M. (2009). Video hand tracking algorithm based on hybrid Camshift and Kalman filter. Application Research of Computers, 26(3), 1163–1165.
Li, Y. (2012, June). Hand gesture recognition using Kinect. In 2012 IEEE 3rd International Conference on Software Engineering and Service Science (ICSESS) (pp. 196–199). IEEE.
Dudani, S. A. (1976). The distance-weighted k-nearest-neighbor rule. IEEE Transactions on Systems, Man, and Cybernetics, 4, 325–327.
Keller, J. M., Gray, M. R., & Givens, J. A. (1985). A fuzzy k-nearest neighbor algorithm. IEEE Transactions on Systems, Man, and Cybernetics, 4, 580–585.
Kollorz, E., Penne, J., Hornegger, J., & Barke, A. (2008). Gesture recognition with a time-of-flight camera. International Journal of Intelligent Systems Technologies and Applications, 5(3), 334.
Chen, Y. T., & Tseng, K. T. (2007, September). Multiple-angle hand gesture recognition by fusing SVM classifiers. In IEEE International Conference on Automation Science and Engineering, 2007. CASE 2007 (pp. 527–530). IEEE.
Dardas, N., Chen, Q., Georganas, N. D., & Petriu, E. M. (2010, October). Hand gesture recognition using bag-of-features and multi-class support vector machine. In 2010 IEEE International Symposium on Haptic Audio-Visual Environments and Games (HAVE) (pp. 1–5). IEEE.
Chen, F. S., Fu, C. M., & Huang, C. L. (2003). Hand gesture recognition using a real-time tracking method and hidden Markov models. Image and Vision Computing, 21(8), 745–758.
Elmezain, M., Al-Hamadi, A., Appenrodt, J., & Michaelis, B. (2009). A hidden markov model-based isolated and meaningful hand gesture recognition. International Journal of Electrical, Computer, and Systems Engineering, 3(3), 156–163.
Waibel, A., Hanazawa, T., Hinton, G., Shikano, K., & Lang, K. J. (1990). Phoneme recognition using time-delay neural networks. In Readings in speech recognition (pp. 393–404).
Hong, P., Turk, M., & Huang, T. S. (2000). Constructing finite state machines for fast gesture recognition. In Proceedings 15th International Conference on Pattern Recognition, 2000 (Vol. 3, pp. 691–694). IEEE.
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Gupta, S., Bagga, S., Sharma, D.K. (2020). Hand Gesture Recognition for Human Computer Interaction and Its Applications in Virtual Reality. In: Gupta, D., Hassanien, A., Khanna, A. (eds) Advanced Computational Intelligence Techniques for Virtual Reality in Healthcare. Studies in Computational Intelligence, vol 875. Springer, Cham. https://doi.org/10.1007/978-3-030-35252-3_5
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