Neural Network Based Gesture Recognition for Elderly Health Care Using Kinect Sensor

  • Sriparna Saha
  • Monalisa Pal
  • Amit Konar
  • Ramadoss Janarthanan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8298)


A simple method to detect gestures revealing muscle and joint pain is described in this paper. Kinect Sensor is used for data acquisition. This sensor only processes twenty joint coordinates in three dimensional space for feature extraction. The recognition part is achieved using a neural network optimized by Levenberg-Marquardt learning rule. A high recognition rate of 91.9% is achieved using the proposed method. This is also better than several algorithms previously used for elder person gesture recognition works.


Elder gesture Kinect sensor neural network 


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  1. 1.
    Halder, A., Rakshit, P., Chakraborty, A., Konar, A., Janarthanan, R.: Emotion recognition from the lip-contour of a subject using artificial bee colony optimization algorithm. In: Panigrahi, B.K., Suganthan, P.N., Das, S., Satapathy, S.C. (eds.) SEMCCO 2011, Part I. LNCS, vol. 7076, pp. 610–617. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  2. 2.
    Chakraborty, A., Konar, A.: Emotional Intelligence: A Cybernetic Approach, vol. 1234. Springer (2009)Google Scholar
  3. 3.
    Saha, S., Ghosh, S., Konar, A., Nagar, A.K.: Gesture Recognition from Indian Classical Dance Using Kinect Sensor. In: 2013 Fifth International Conference on Computational Intelligence, Communication Systems and Networks (CICSyN), pp. 3–8 (2013)Google Scholar
  4. 4.
    Zhou, Z., Dai, W., Eggert, J., Giger, J.T., Keller, J., Rantz, M., He, Z.: A real-time system for in-home activity monitoring of elders. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC, pp. 6115–6118 (2009)Google Scholar
  5. 5.
    Diraco, G., Leone, A., Siciliano, P.: An active vision system for fall detection and posture recognition in elderly healthcare. In: Design, Automation & Test in Europe Conference & Exhibition (DATE), pp. 1536–1541 (2010)Google Scholar
  6. 6.
    Liu, Y., Zhang, Z., Li, A., Wang, M.: View independent human posture identification using Kinect. In: 2012 5th International Conference on Biomedical Engineering and Informatics (BMEI), pp. 1590–1593 (2012)Google Scholar
  7. 7.
    Leyvand, T., Meekhof, C., Wei, Y.-C., Sun, J., Guo, B.: Kinect identity: Technology and experience. Computer 44(4), 94–96 (2011)CrossRefGoogle Scholar
  8. 8.
    Tanabe, R., Cao, M., Murao, T., Hashimoto, H.: Vision based object recognition of mobile robot with Kinect 3D sensor in indoor environment. In: 2012 Proceedings of SICE Annual Conference (SICE), pp. 2203–2206 (2012)Google Scholar
  9. 9.
    Le, T.-L., Nguyen, M.-Q., Nguyen, T.-T.-M.: Human posture recognition using human skeleton provided by Kinect. In: 2013 International Conference on Computing, Management and Telecommunications (ComManTel), pp. 340–345 (2013)Google Scholar
  10. 10.
    Zhang, Z.: Microsoft kinect sensor and its effect. IEEE Multimedia 19(2), 4–10 (2012)CrossRefGoogle Scholar
  11. 11.
    Dutta, T.: Evaluation of the KinectTM sensor for 3-D kinematic measurement in the workplace. Applied Ergonomics 43(4), 645–649 (2012)CrossRefGoogle Scholar
  12. 12.
    Konar, A.: Artificial intelligence and soft computing: behavioral and cognitive modeling of the human brain, vol. 1. CRC Press (1999)Google Scholar
  13. 13.
    Konar, A.: Computational intelligence: principles, techniques and applications. Springer (2005)Google Scholar
  14. 14.
    Kermani, B.G., Schiffman, S.S., Nagle, H.T.: Performance of the Levenberg–Marquardt neural network training method in electronic nose applications. Sensors and Actuators B: Chemical 110(1), 13–22 (2005)CrossRefGoogle Scholar
  15. 15.
    Lera, G., Pinzolas, M.: Neighborhood based Levenberg-Marquardt algorithm for neural network training. IEEE Transactions on Neural Networks 13(5), 1200–1203 (2002)CrossRefGoogle Scholar
  16. 16.
    Solaro, J.: The Kinect Digital Out-of-Box Experience Computer, pp. 97–99 (2011)Google Scholar
  17. 17.
    Zhuang, H., Zhao, B., Ahmad, Z., Chen, S., Low, K.S.: 3D depth camera based human posture detection and recognition Using PCNN circuits and learning-based hierarchical classifier. In: The 2012 International Joint Conference on Neural Networks (IJCNN), pp. 1–5 (2012)Google Scholar
  18. 18.
    Li, S., Kwok, J.T., Wang, Y.: Multifocus image fusion using artificial neural networks. Pattern Recognition Letters 23(8), 985–997 (2002)CrossRefzbMATHGoogle Scholar
  19. 19.
    Brown, D.E., Corruble, V., Pittard, C.L.: A comparison of decision tree classifiers with backpropagation neural networks for multimodal classification problems. Pattern Recognition 26(6), 953–961 (1993)CrossRefGoogle Scholar
  20. 20.
    Sevakula, R.K., Verma, N.K.: Support vector machine for large databases as classifier. In: Panigrahi, B.K., Das, S., Suganthan, P.N., Nanda, P.K. (eds.) SEMCCO 2012. LNCS, vol. 7677, pp. 303–313. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  21. 21.
    Das, S., Halder, A., Bhowmik, P., Chakraborty, A., Konar, A., Nagar, A.K.: Voice and facial expression based classification of emotion using linear support vector machine. In: 2009 Second International Conference on Developments in eSystems Engineering (DESE), pp. 377–384 (2009)Google Scholar
  22. 22.
    Cortes, C., Vapnik, V.: Support vector machine. Machine Learning 20(3), 273–297 (1995)zbMATHGoogle Scholar
  23. 23.
    Zhan, Y., Shen, D.: Design efficient support vector machine for fast classification. Pattern Recognition 38(1), 157–161 (2005)CrossRefGoogle Scholar
  24. 24.
    Wu, Y., Ianakiev, K., Govindaraju, V.: Improved<i> k</i>-nearest neighbor classification. Pattern Recognition 35(10), 2311–2318 (2002)CrossRefzbMATHGoogle Scholar
  25. 25.
    Cover, T., Hart, P.: Nearest neighbor pattern classification. IEEE Transactions on Information Theory 13(1), 21–27 (1967)CrossRefzbMATHGoogle Scholar
  26. 26.
    Wang, J., Neskovic, P., Cooper, L.N.: Improving nearest neighbor rule with a simple adaptive distance measure. Pattern Recognition Letters 28(2), 207–213 (2007)CrossRefGoogle Scholar
  27. 27.
    Vidal Ruiz, E.: An algorithm for finding nearest neighbours in (approximately) constant average time. Pattern Recognition Letters 4(3), 145–157 (1986)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Sriparna Saha
    • 1
  • Monalisa Pal
    • 1
  • Amit Konar
    • 1
  • Ramadoss Janarthanan
    • 2
  1. 1.Electronics & Telecommunication Engineering DepartmentJadavpur UniversityIndia
  2. 2.Computer Science and Engineering DepartmentTJS Engineering CollegeIndia

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