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Detecting Unusual Human Activities Using GPU-Enabled Neural Network and Kinect Sensors

  • Ricardo BritoEmail author
  • Simon Fong
  • Wei Song
  • Kyungeun Cho
  • Chintan Bhatt
  • Dmitry Korzun
Chapter
Part of the Studies in Big Data book series (SBD, volume 23)

Abstract

Graphic Processing Units (GPU) and kinetic sensors are promising devices of Internet of Things (IoT) computing environments in various application domains, including mobile healthcare. In this chapter a novel training/testing process for building/testing a classification model for unusual human activities (UHA) using ensembles of Neural Networks running on NVIDIA GPUs is proposed. Traditionally, UHA is done by a classifier that learns what activities a person is doing by training with skeletal data obtained from a motion sensor such as Microsoft Kinect [1]. These skeletal data are the spatial coordinates (x, y, z) of different parts of the human body. The numeric information forms time series, temporal records of movement sequences that can be used for training an ensemble of Neural Networks. In addition to the spatial features that describe current positions in the skeletal data, new features called shadow features are used to improve the supervised learning efficiency of the ensemble of Neural Networks running on an NVIDIA GPU card. Shadow features are inferred from the dynamics of body movements, thereby modelling the underlying momentum of the performed activities. They provide extra dimensions of information for characterizing activities in the classification process and thus significantly improving the accuracy. We show that the accuracy of using a Neural Network as a classifier on a data set with shadow features can still be further increased when more than one Neural Network is used, forming an ensemble of networks. In order to accelerate the processing speed of an ensemble of Neural Networks, the model proposed is designed and optimized to run on NIVDIA GPUs with CUDA.

Keywords

Unusual human activities Neural network Machine learning GPU Classification Healthcare Internet of Things 

Notes

Acknowledgments

The authors are thankful for the financial support from the Research Grant called “A scalable data stream mining methodology: stream-based holistic analytics and reasoning in parallel”, Grant no. FDCT-126/2014/A3, offered by the University of Macau, FST, RDAO and the FDCT of Macau SAR government. The work of D. Korzun is financially supported by Russian Fund for Basic Research (RFBR) according to research project # 16-07-01289.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Ricardo Brito
    • 1
    Email author
  • Simon Fong
    • 1
  • Wei Song
    • 2
  • Kyungeun Cho
    • 3
  • Chintan Bhatt
    • 4
  • Dmitry Korzun
    • 5
  1. 1.Department of Computer and Information ScienceUniversity of MacauTaipa, Macau SARChina
  2. 2.Department of Computer Science and TechnologyNorth China University of TechnologyBeijingChina
  3. 3.Department of Multimedia EngineeringDongguk UniversitySeoulSouth Korea
  4. 4.Department of Computer EngineeringCharotar University of Science and Technology (CHARUSAT)ChangaIndia
  5. 5.Department of Computer SciencePetrozavodsk State UniversityPetrozavodsk, Republic of KareliaRussia

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