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

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Swarm, Evolutionary, and Memetic Computing (SEMCCO 2013)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8298))

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Abstract

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.

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Author information

Authors and Affiliations

  1. Electronics & Telecommunication Engineering Department, Jadavpur University, India

    Sriparna Saha, Monalisa Pal & Amit Konar

  2. Computer Science and Engineering Department, TJS Engineering College, India

    Ramadoss Janarthanan

Authors
  1. Sriparna Saha
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  2. Monalisa Pal
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  3. Amit Konar
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  4. Ramadoss Janarthanan
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Editor information

Editors and Affiliations

  1. Electrical Engineering Dept, IIT Delhi, Hauz Khas, 110016, New Delhi, India

    Bijaya Ketan Panigrahi

  2. School of Electrical and Electronic Engineering, Nanyang Technological University, 639798, Singapore

    Ponnuthurai Nagaratnam Suganthan

  3. Electronics and Communication Sciences Unit, Indian Statistical Institute, 700108, Kolkata, India

    Swagatam Das

  4. SRM Engineering College, Department of Electrical and Electronics Engineering, SRM University, Potheri, Kattankulathur, 603203, Kancheepuram, Tamil Nadu, India

    Shubhransu Sekhar Dash

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Saha, S., Pal, M., Konar, A., Janarthanan, R. (2013). Neural Network Based Gesture Recognition for Elderly Health Care Using Kinect Sensor. In: Panigrahi, B.K., Suganthan, P.N., Das, S., Dash, S.S. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2013. Lecture Notes in Computer Science, vol 8298. Springer, Cham. https://doi.org/10.1007/978-3-319-03756-1_34

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  • DOI: https://doi.org/10.1007/978-3-319-03756-1_34

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-03755-4

  • Online ISBN: 978-3-319-03756-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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