UM
Detecting Unusual Human Activities Using GPU-Enabled Neural Network and Kinect Sensors
Brito, Ricardo; Fong, Simon; Song, Wei; Cho, Kyungeun; Bhatt, Chintan; Korzun, Dmitry; Bhatt, C; Dey, N; Ashour, AS
2017
Source PublicationINTERNET OF THINGS AND BIG DATA TECHNOLOGIES FOR NEXT GENERATION HEALTHCARE
ISSN2197-6503
Volume23Pages:359-388
AbstractGraphic 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.
KeywordUnusual human activities Neural network Machine learning GPU Classification Healthcare Internet of Things
DOI10.1007/978-3-319-49736-5_15
URLView the original
Indexed ByBHCI ; BSCI
Language英语
WOS Research AreaComputer Science ; Health Care Sciences & Services
WOS SubjectComputer Science, Information Systems ; Computer Science, Interdisciplinary Applications ; Health Policy & Services
WOS IDWOS:000414931400016
PublisherSPRINGER INTERNATIONAL PUBLISHING AG
The Source to ArticleWOS
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Cited Times [WOS]:1   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionUniversity of Macau
Recommended Citation
GB/T 7714
Brito, Ricardo,Fong, Simon,Song, Wei,et al. Detecting Unusual Human Activities Using GPU-Enabled Neural Network and Kinect Sensors[J]. INTERNET OF THINGS AND BIG DATA TECHNOLOGIES FOR NEXT GENERATION HEALTHCARE,2017,23:359-388.
APA Brito, Ricardo.,Fong, Simon.,Song, Wei.,Cho, Kyungeun.,Bhatt, Chintan.,...&Ashour, AS.(2017).Detecting Unusual Human Activities Using GPU-Enabled Neural Network and Kinect Sensors.INTERNET OF THINGS AND BIG DATA TECHNOLOGIES FOR NEXT GENERATION HEALTHCARE,23,359-388.
MLA Brito, Ricardo,et al."Detecting Unusual Human Activities Using GPU-Enabled Neural Network and Kinect Sensors".INTERNET OF THINGS AND BIG DATA TECHNOLOGIES FOR NEXT GENERATION HEALTHCARE 23(2017):359-388.
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