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Probability-Induced Distance-Based Gesture Matching for Health care Using Microsoft’s Kinect Sensor

  • Monalisa Pal
  • Sriparna Saha
  • Amit Konar
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 518)

Abstract

Detection of 14 healthcare-related gestures due to pain at different body parts is the target area of this work using Kinect sensor. The novelty of our work lies in suppressing the problem of compensation by the use of probability while using similarity matching technique for gesture recognition. The adopted method enhances the matching accuracy for all the similarity measures. A shared probability and similarity measure-based metric has been defined as the matching index. This unique technique contributes to field of health care under static gesture recognition as an application of machine learning with a high accuracy of 99.1071% in 0.0126 s using probability-induced city-block distance.

Keywords

Distance matching Health care Kinect sensor Probability 

Notes

Acknowledgements

The research work is supported by the University Grants Commission, India, University with Potential for Excellence Program (Phase II) in Cognitive Science, Jadavpur University and University Grants Commission (UGC) for providing fellowship to the second author.

References

  1. 1.
    S. Saha, M. Pal, A. Konar, and R. Janarthanan, “Neural Network Based Gesture Recognition for Elderly Health Care Using Kinect Sensor,” in Swarm, Evolutionary, and Memetic Computing, Springer, 2013, pp. 376–386.Google Scholar
  2. 2.
    M. Pal, S. Saha, and A. Konar, “A Fuzzy C Means Clustering Approach for Gesture Recognition in Healthcare,” Knee, vol. 1, p. C7.Google Scholar
  3. 3.
    M. Parajuli, D. Tran, W. Ma, and D. Sharma, “Senior health monitoring using Kinect,” in Communications and Electronics (ICCE), 2012 Fourth International Conference on, 2012, pp. 309–312.Google Scholar
  4. 4.
    T.-L. Le, M.-Q. Nguyen, and T.-T.-M. Nguyen, “Human posture recognition using human skeleton provided by Kinect,” in Computing, Management and Telecommunications (ComManTel), 2013 International Conference on, 2013, pp. 340–345.Google Scholar
  5. 5.
    X. Yu, L. Wu, Q. Liu, and H. Zhou, “Children tantrum behaviour analysis based on Kinect sensor,” in Intelligent Visual Surveillance (IVS), 2011 Third Chinese Conference on, 2011, pp. 49–52.Google Scholar
  6. 6.
    B. Galna, G. Barry, D. Jackson, D. Mhiripiri, P. Olivier, and L. Rochester, “Accuracy of the Microsoft Kinect sensor for measuring movement in people with Parkinson’s disease,” Gait Posture, 2014.Google Scholar
  7. 7.
    M. Pal, S. Saha and A. Konar, “Distance Matching Based Gesture Recognition For Healthcare Using Microsoft’s Kinect Sensor,” in International Conference on Microelectronics, Computing and Communication, IEEE, 2016, (to be published).Google Scholar
  8. 8.
    G. Yu, S. Shao, and B. Luo, “Mining Crime Data by Using New Similarity Measure,” in Genetic and Evolutionary Computing, 2008. WGEC’08. Second International Conference on, 2008, pp. 389–392.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Electronics & Tele-Communication Engineering DepartmentJadavpur UniversityKolkataIndia

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