Accelerator-based human activity recognition using voting technique with NBTree and MLP classifiers
In 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 attac...
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Insight Society
2023
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| _version_ | 1833352077502316544 |
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| author | Azmi M.S.M. Sulaiman M.N. |
| author2 | 36994351200 |
| author_facet | 36994351200 Azmi M.S.M. Sulaiman M.N. |
| author_sort | Azmi M.S.M. |
| building | UNITEN Library |
| collection | Institutional Repository |
| content_provider | Universiti Tenaga Nasional |
| content_source | UNITEN Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | In 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. |
| format | Article |
| id | my.uniten.dspace-23474 |
| institution | Universiti Tenaga Nasional |
| publishDate | 2023 |
| publisher | Insight Society |
| record_format | dspace |
| spelling | my.uniten.dspace-234742023-05-29T14:40:49Z Accelerator-based human activity recognition using voting technique with NBTree and MLP classifiers Azmi M.S.M. Sulaiman M.N. 36994351200 22434244300 In 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. Final 2023-05-29T06:40:49Z 2023-05-29T06:40:49Z 2017 Article 10.18517/ijaseit.7.1.1790 2-s2.0-85013888551 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85013888551&doi=10.18517%2fijaseit.7.1.1790&partnerID=40&md5=bc55da2d899b0942632db44f49adcd5b https://irepository.uniten.edu.my/handle/123456789/23474 7 1 146 152 All Open Access, Hybrid Gold Insight Society Scopus |
| spellingShingle | Azmi M.S.M. Sulaiman M.N. Accelerator-based human activity recognition using voting technique with NBTree and MLP classifiers |
| title | Accelerator-based human activity recognition using voting technique with NBTree and MLP classifiers |
| title_full | Accelerator-based human activity recognition using voting technique with NBTree and MLP classifiers |
| title_fullStr | Accelerator-based human activity recognition using voting technique with NBTree and MLP classifiers |
| title_full_unstemmed | Accelerator-based human activity recognition using voting technique with NBTree and MLP classifiers |
| title_short | Accelerator-based human activity recognition using voting technique with NBTree and MLP classifiers |
| title_sort | accelerator-based human activity recognition using voting technique with nbtree and mlp classifiers |
| url_provider | http://dspace.uniten.edu.my/ |