Gesture Recognition from Data Streams of Human Motion Sensor Using Accelerated PSO Swarm Search Feature Selection Algorithm
Simon Fong1; Justin Liang1; Iztok Fister Jr.2; Iztok Fister2; Sabah Mohammed3
2015
Source PublicationJournal of Sensors
ISSN1687-725X
Volume2015
Abstract

Human motion sensing technology gains tremendous popularity nowadays with practical applications such as video surveillance for security, hand signing, and smart-home and gaming. These applications capture human motions in real-time from video sensors, the data patterns are nonstationary and ever changing. While the hardware technology of such motion sensing devices as well as their data collection process become relatively mature, the computational challenge lies in the real-time analysis of these live feeds. In this paper we argue that traditional data mining methods run short of accurately analyzing the human activity patterns from the sensor data stream. The shortcoming is due to the algorithmic design which is not adaptive to the dynamic changes in the dynamic gesture motions. The successor of these algorithms which is known as data stream mining is evaluated versus traditional data mining, through a case of gesture recognition over motion data by using Microsoft Kinect sensors. Three different subjects were asked to read three comic strips and to tell the stories in front of the sensor. The data stream contains coordinates of articulation points and various positions of the parts of the human body corresponding to the actions that the user performs. In particular, a novel technique of feature selection using swarm search and accelerated PSO is proposed for enabling fast preprocessing for inducing an improved classification model in real-time. Superior result is shown in the experiment that runs on this empirical data stream. The contribution of this paper is on a comparative study between using traditional and data stream mining algorithms and incorporation of the novel improved feature selection technique with a scenario where different gesture patterns are to be recognized from streaming sensor data.

DOIhttp://dx.doi.org/10.1155/2015/205707
URLView the original
Indexed BySCI
Language英语
WOS Research AreaEngineering ; Instruments & Instrumentation
WOS SubjectEngineering, Electrical & Electronic ; Instruments & Instrumentation
WOS IDWOS:000352884800001
PublisherHINDAWI PUBLISHING CORP, 410 PARK AVENUE, 15TH FLOOR, #287 PMB, NEW YORK, NY 10022 USA
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Citation statistics
Cited Times [WOS]:6   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Corresponding AuthorSabah Mohammed
Affiliation1.Department of Computer and Information Science, University of Macau, Macau
2.Faculty of Electrical Engineering and Computer Science, University of Maribor, 2000 Maribor, Slovenia
3.Department of Computer Science, Lakehead University, 955 Oliver Road, Thunder Bay, ON, Canada P7B 5E1
First Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Simon Fong,Justin Liang,Iztok Fister Jr.,et al. Gesture Recognition from Data Streams of Human Motion Sensor Using Accelerated PSO Swarm Search Feature Selection Algorithm[J]. Journal of Sensors,2015,2015.
APA Simon Fong,Justin Liang,Iztok Fister Jr.,Iztok Fister,&Sabah Mohammed.(2015).Gesture Recognition from Data Streams of Human Motion Sensor Using Accelerated PSO Swarm Search Feature Selection Algorithm.Journal of Sensors,2015.
MLA Simon Fong,et al."Gesture Recognition from Data Streams of Human Motion Sensor Using Accelerated PSO Swarm Search Feature Selection Algorithm".Journal of Sensors 2015(2015).
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