Gesture interaction for coronary heart diseases based on wavelet transform and semi-continuous hidden markov model in augmented reality

被引：0
作者：
Gao M. [1 ,2 ]
Chen Y. [1 ]
Zhang D. [1 ,3 ]
Li Z. [1 ,4 ]
Jiang S. [1 ]
Lv S. [1 ]
Lu R. [1 ]
Lu J. [1 ]
Huang C. [1 ]
机构：
[1] School of Computer Engineering and Science, Shanghai University, Shanghai
[2] The 32nd Research Institute, China Electronics Technology Group Corporation, Shanghai City
[3] College of Digital Arts, Shanghai University, Shanghai
[4] Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai
来源：
Chen, Yimin (ymchen@mail.shu.edu.cn) | 1600年 / Bentham Science Publishers卷 / 10期
关键词：
Augmented reality; Coronary heart disease; Gesture interaction; Gesture recognition; Semi-continuous hidden markov model; Wavelet transform;
D O I：
10.2174/2352096510666170601123551
中图分类号：
学科分类号：
摘要：
Background: Most doctors are used to locate the vascular stenosis position first then clinically estimate vascular stenosis by the CAG images instead of using mouse, keyboard and computer during the preoperative diagnosis. Therefore, it is necessary to have an effective, intuitive and natural method to replace the existing preoperative diagnosis mode for diagnosing the coronary heart disease. Methods: This work presents the gesture interactive diagnostic method for coronary heart disease based on wavelet transform and Semi-continuous Hidden Markov Model in Augmented Reality. Firstly, the dynamic gesture trajectory is analyzed by using K-means cluster algorithm to determine the gesture trajectory plane. Secondly, the integrity of the trajectory is preserved on the sensor plane by employing trajectory projection and space rotation transformation. Thirdly, the features include the azimuth of gesture trajectory and the changes of hand shape are extracted. Meanwhile, the azimuth of gesture trajectory is analyzed by utilizing wavelet transform and the singularity of the trajectory is detected to compute the state number of SCHMM for all the dynamic gestures. The gesture training is completed by employing Baum-Welch algorithm with multi-features and multi-observation sequences. Ultimately, gesture recognition is realized by using Viterbi algorithm based on self-learning mechanism. It is then applied into the gesture interactive diagnosis of coronary heart disease and the diagnosis of the vascular stenosis are intuitively and naturally realized with augmented reality and gesture interactive techniques. Results: The number of states of dynamic gesture is determined by using wavelet transform which precisely detect singular points of gesture trajectory. The recognition rate of dynamic gesture is average of 96.1% and the semi-dynamic gesture is average of 92.6%, the total recognition rate is average of 95.6%. The dynamic gesture recognition rate of the proposed method is 7% higher than the traditional method. The intuitive and natural gesture interactive experiments and the affirmed measuring results are approved by surgeons. Conclusions: The proposed method is a feasible, usable and reliable gesture interactive diagnostic method for coronary heart disease based on wavelet transform and Semi-continuous Hidden Markov Model in Augmented Reality. The results demonstrate that the proposed method could improve the gesture recognition rate, extend the gesture manipulation space and expand the diagnostic method for coronary heart diseases. © 2017 Bentham Science Publishers.
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页码：150 / 165
页数：15
相关论文
共 46 条
  • [1] Global Status Report on Noncommunicable Diseases 2014, pp. 29-30, (2015)
  • [2] Miller J.M., Rochitte C.E., Deway M., Arbab-Zadeh A., Niinuma H., Gottlieb I., Paul N., Clouse M.E., Shapiro E.P., Hoe J., Lardo A.C., Bush D.E., De Roos A., Cox C., Brinker J., Lima J.A., Diagnostic performance of coronary angiography by 64-row CT, N. Engl. J. Med., 359, pp. 2324-2336, (2008)
  • [3] Fine J.J., Hopkins C.B., Ruff N., Newton F.C., Comparison of accuracy of 64-slice cardiovascular computed tomography with coronary angiography in patients with suspected coronary artery disease, Am. J. Cardiol, 97, pp. 173-174, (2006)
  • [4] Matjaz K., Ciril G., Transductive machine learning for reliable medical diagnostics, J. Med. Syst., 29, 1, pp. 13-32, (2005)
  • [5] Mandal I., Sairam N., Accurate prediction of coronary artery disease using reliable diagnosis system, J. Med. Syst., 36, 5, pp. 3353-3373, (2012)
  • [6] Lahsasna A., Ainon R.N., Zainuddin R., Bulgiba A., Design of a fuzzy-based decision support system for coronary heart disease diagnosis, J. Med. Systems, 36, 5, pp. 3293-3306, (2012)
  • [7] Karabulut E.M., Ibrikci T., Effective diagnosis of coronary artery disease using the rotation forest ensemble method, J. Med. Systems, 36, 5, pp. 3011-3018, (2012)
  • [8] Rautaray S.S., Agrawal A., Vision based hand gesture recognition for human computer interaction: A survey, Artifi. Intelli. Rev., 43, 1, pp. 1-54, (2015)
  • [9] Pisharady P.K., Saerbeck M., Recent methods and databases in vision-based hand gesture recognition: A review, Comput. Vis. Image Understand., 141, pp. 152-165, (2015)
  • [10] Pan J.S., Lo C.C., Tsai S.H., Lin B.S., Design of novel noncontact multimedia controller for disability by using visual stimulus, Comput. Method. Programs Biomed., 122, 3, pp. 384-392, (2015)
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