Publication:
Accelerator-based human activity recognition using voting technique with NBTree and MLP classifiers

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dc.citedby18
dc.contributor.authorAzmi M.S.M.en_US
dc.contributor.authorSulaiman M.N.en_US
dc.contributor.authorid36994351200en_US
dc.contributor.authorid22434244300en_US
dc.date.accessioned2023-05-29T06:40:49Z
dc.date.available2023-05-29T06:40:49Z
dc.date.issued2017
dc.description.abstractIn evolution and ubiquitous computing systems, accelerometer-based human activity recognition has huge potential in a large number of application domains. Accelerometer-based human activity recognition aims to identify physical activities performed by human using accelerometer; a sensor device attached to the body and returns an actual valued estimate of acceleration along the x, y- and z-axes from which the sensor location can be estimated. In this study, an accelerator-based activity recognition model using voting technique was proposed. Two machine learning classifiers, Naive Bayes Tree (NBTree) and Multilayer Perceptron (MLP), were used as ensemble classifiers in the voting technique. To evaluate the proposed voting technique, the performance of selected individual classifiers and existing voting technique was first examined, followed by the experiment to determine the performance of the proposed model. All of the experiments were performed using a standard dataset called Wireless Sensor Data Mining involving six physical human activities; jogging, walking, walking towards upstairs, walking towards downstairs, sitting and stand still. Results showed that the proposed voting technique with NBTree and MLP ensemble classifiers outperformed other individual classifiers and another previously suggested voting technique for accelerometer-based human activity recognition.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.18517/ijaseit.7.1.1790
dc.identifier.epage152
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85013888551
dc.identifier.spage146
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85013888551&doi=10.18517%2fijaseit.7.1.1790&partnerID=40&md5=bc55da2d899b0942632db44f49adcd5b
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/23474
dc.identifier.volume7
dc.publisherInsight Societyen_US
dc.relation.ispartofAll Open Access, Hybrid Gold
dc.sourceScopus
dc.sourcetitleInternational Journal on Advanced Science, Engineering and Information Technology
dc.titleAccelerator-based human activity recognition using voting technique with NBTree and MLP classifiersen_US
dc.typeArticleen_US
dspace.entity.typePublication
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