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Fusing wearable and remote sensing data streams by fast incremental learning with swarm decision table for human activity recognition
Tengyue Li1; Simon Fong1; Kelvin K.L. Wong2; Ying Wu3; Xin-she Yang4; Xuqi Li5
2020-02-14
Source PublicationInformation Fusion
ISSN1566-2535
Volume60Pages:41-64
Abstract

Human activity recognition (HAR) by machine learning finds wide applications ranging from posture monitoring for healthcare and rehabilitation to suspicious or dangerous actions detection for security surveillance. Infrared cameras such as Microsoft Kinect and wearable sensors have been the two most adopted devices for collecting data for measuring the bodily movements. These two types of sensors generally are categorized as contactless sensing and contact sensing respectively. Due to hardware limitation, each of the two sensor types has their inherent limitations. One most common problem associating with contactless sensing like Kinect is the distance and indirect angle between the camera and the subject. For wearable sensor, it is limited in recognizing complex human activities. In this paper, a novel data fusion framework is proposed for combining data which are collected from both sensors with the aim of enhancing the HAR accuracy. Kinect is able to capture details of bodily movements from complex activities, but the accuracy is dependent heavily on the angle of view; wearable sensor is relatively primitive in gathering spatial data but reliable for detecting basic movements. Fusing the data from the two sensor types enables complimenting each other by their unique strengths. In particular, a new scheme using incremental learning with decision table coupled with swarm-based feature selection is proposed in our framework for achieving fast and accurate HAR by fusing data of two sensors. Our experiment results show that HAR accuracy could be improved from 23.51% to 68.35% in a case of almost 90 degrees slanted view of Kinect sensing while a wearing sensor is used at the same time. The swarm feature selection in general is shown to enhance the HAR performance compared to standard feature selection method. The experiment results reported here contribute to the possibilities of using hybridized sensors from the machine learning perspective.

KeywordKinect Depth Sensor Wearable Sensor Data Mining Classification Model Feature Selection
DOI10.1016/j.inffus.2020.02.001
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Theory & Methods
WOS IDWOS:000531553100005
PublisherELSEVIER, RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
Scopus ID2-s2.0-85081134400
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Citation statistics
Cited Times [WOS]:8   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Corresponding AuthorSimon Fong; Kelvin K.L. Wong
Affiliation1.Department of Computer and Information Science,University of Macau,Macau SAR,China
2.School of Electrical and Electronic Engineering,The University of Adelaide,5000,Australia
3.School of Nursing,Capital Medical University,Beijing,China
4.Department of Design Engineering and Mathematics,Middlesex University,London,United Kingdom
5.School of Informatics,University of Edinburgh,Edinburgh,United Kingdom
First Author AffilicationUniversity of Macau
Corresponding Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Tengyue Li,Simon Fong,Kelvin K.L. Wong,et al. Fusing wearable and remote sensing data streams by fast incremental learning with swarm decision table for human activity recognition[J]. Information Fusion,2020,60:41-64.
APA Tengyue Li,Simon Fong,Kelvin K.L. Wong,Ying Wu,Xin-she Yang,&Xuqi Li.(2020).Fusing wearable and remote sensing data streams by fast incremental learning with swarm decision table for human activity recognition.Information Fusion,60,41-64.
MLA Tengyue Li,et al."Fusing wearable and remote sensing data streams by fast incremental learning with swarm decision table for human activity recognition".Information Fusion 60(2020):41-64.
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