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Novel evolutionary-EAC instance-learning-based algorithm for fast data stream mining in assisted living with extreme connectivity
Hu, Shimin1; Fong, Simon1; Song, Wei2; Cho, Kyungeun3; Millham, Richard C.4; Fiaidhi, Jinan5
2021-07-01
Source PublicationComputing
ISSN0010-485X
Volume103Issue:7Pages:1519-1543
Abstract

In modern healthcare, sensing technologies such as IoT empower the quality of assisted living service by knowing what a resident is doing in real-time. Using extreme connectivity and cloud computing in a smart home, where a collection of sensors is installed, the sensors sample continuously from the movements of the resident as well as ambient data from the surrounding inside the house. Automatic human activity recognition of the resident's activities is one of the key components of assisted living in smart home. For monitoring in-home safety, the ability in recognizing abnormal activities such as accident, falling, acute disease attack (e.g. asthma, stroke, etc.), fainting, wobbling, is particularly important. The detection and machine learning process must be both accurate and fast, to cope with the real-time activity recognition. To this end, a novel streamlined sensor data processing method is proposed called Evolutionary Expand-and-Contract Instance-based Learning algorithm (EEAC-IBL). The multivariate data stream is first expanded into many subspaces, then the subspaces which are corresponding to the characteristics of the features are selected and condensed into a significant feature subset. The selection operates scholastically instead of deterministically by evolutionary optimization which approximates the best subgroup. Followed by data stream mining, the machine learning for activity recognition is done on the fly. This approach is unique and suitable for such extreme connectivity scenario where precise feature selection is not required, and the relative importance of each feature among the sensor data changes over time. This stochastic approximation method is fast and accurate, offering an alternative to traditional machine learning method for smart home activity recognition application. Our experimental results show computing advantages over other classical approaches.

KeywordAssisted Living Extreme Connectivity Forecasting Human Activity Recognition Iot Data Analysis Regression
DOI10.1007/s00607-020-00899-2
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaComputer Science
WOS SubjectComputer Science, Theory & Methods
WOS IDWOS:000628489800001
Scopus ID2-s2.0-85102682377
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Cited Times [WOS]:1   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionInstitute of Chinese Medical Sciences
Corresponding AuthorFong, Simon
Affiliation1.Department of Computer and Information Science, University of Macau, Taipa, Macao
2.School of Computer Science, North China University of Technology, Beijing, China
3.Department of Multimedia Engineering, Dongguk University, Seoul, South Korea
4.ICT and Society Group, Durban University of Technology, Durban, South Africa
5.Lakehead University, Thunder Bay, Canada
First Author AffilicationUniversity of Macau
Corresponding Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Hu, Shimin,Fong, Simon,Song, Wei,et al. Novel evolutionary-EAC instance-learning-based algorithm for fast data stream mining in assisted living with extreme connectivity[J]. Computing,2021,103(7):1519-1543.
APA Hu, Shimin,Fong, Simon,Song, Wei,Cho, Kyungeun,Millham, Richard C.,&Fiaidhi, Jinan.(2021).Novel evolutionary-EAC instance-learning-based algorithm for fast data stream mining in assisted living with extreme connectivity.Computing,103(7),1519-1543.
MLA Hu, Shimin,et al."Novel evolutionary-EAC instance-learning-based algorithm for fast data stream mining in assisted living with extreme connectivity".Computing 103.7(2021):1519-1543.
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