A new machine learning approach based on spatial fuzzy data correlation for recognizing sports activities
Wearable sensors (WS) play a vital role in health assistance to improve the patient monitoring process. However, the existing data collection process faces difficulties in error corrections, rehabilitation, and training validations. Therefore, the data analysis requires additional effort to reduce t...
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| Format: | Article |
| Language: | English |
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De Gruyter
2024-11-01
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| Series: | Demonstratio Mathematica |
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| Online Access: | https://doi.org/10.1515/dema-2023-0261 |
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| author | Xu Pengru Zhou Junhui Kausar Nasreen Lin Chunlei Lu Qianqian Ghaderpour Ebrahim Pamucar Dragan Zadeh Ardashir M. |
| author_facet | Xu Pengru Zhou Junhui Kausar Nasreen Lin Chunlei Lu Qianqian Ghaderpour Ebrahim Pamucar Dragan Zadeh Ardashir M. |
| author_sort | Xu Pengru |
| collection | DOAJ |
| description | Wearable sensors (WS) play a vital role in health assistance to improve the patient monitoring process. However, the existing data collection process faces difficulties in error corrections, rehabilitation, and training validations. Therefore, the data analysis requires additional effort to reduce the overall problems in sports rehabilitation. The existing research difficulties are overcome by applying the proposed spatial data correlation with a support vector machine (SDC-SVM). The algorithm uses the hyperplane function that recognizes sportsperson activities and improves overall activity recognition efficiency. The sensor data are analyzed according to the input margin, and the classification process is performed. In addition, feature correlation and input size are considered to maximize the overall classification procedure of WS data correlation using the size and margin of the input and previously stored data. In both the differentiation and classification instances, the spatiotemporal features of data are extracted and analyzed using support vectors. The proposed SDC-SVM method can improve recognition accuracy, F1 score, and computing time for the varying WS inputs, classifications, and subjects. |
| format | Article |
| id | doaj-art-62d48a41305b4b29b4bd3c848ffee115 |
| institution | OA Journals |
| issn | 2391-4661 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Demonstratio Mathematica |
| spelling | doaj-art-62d48a41305b4b29b4bd3c848ffee1152025-08-20T02:23:35ZengDe GruyterDemonstratio Mathematica2391-46612024-11-0157133935010.1515/dema-2023-0261A new machine learning approach based on spatial fuzzy data correlation for recognizing sports activitiesXu Pengru0Zhou Junhui1Kausar Nasreen2Lin Chunlei3Lu Qianqian4Ghaderpour Ebrahim5Pamucar Dragan6Zadeh Ardashir M.7Department of Public Physical Education, Zhejiang Guangsha Vocational and Technical University of Construction, Dongyang322100, ChinaDepartment of Public Physical Education, Zhejiang Guangsha Vocational and Technical University of Construction, Dongyang322100, ChinaDepartment of Mathematics, Faculty of Arts and Science, Yildiz Technical University, 34220, Esenler, Istanbul, TurkeyDepartment of Education and Engineering, Zhejiang Guangsha Vocational and Technical University of Construction, Dongyang322100, ChinaDepartment of Education and Engineering, Administrative Office, Zhejiang Guangsha Vocational and Technical University of Construction, Dongyang322100, ChinaDepartment of Earth Sciences, Sapienza University of Rome, Piazzale Aldo-Moro, 5, 00185Rome, ItalyDepartment of Operations Research and Statistics, Faculty of Organizational Sciences, University of Belgrade, Belgrade, SerbiaMultidisciplinary Centre for Infrastructure Engineering, Shenyang University of Technology, Shenyang110870, ChinaWearable sensors (WS) play a vital role in health assistance to improve the patient monitoring process. However, the existing data collection process faces difficulties in error corrections, rehabilitation, and training validations. Therefore, the data analysis requires additional effort to reduce the overall problems in sports rehabilitation. The existing research difficulties are overcome by applying the proposed spatial data correlation with a support vector machine (SDC-SVM). The algorithm uses the hyperplane function that recognizes sportsperson activities and improves overall activity recognition efficiency. The sensor data are analyzed according to the input margin, and the classification process is performed. In addition, feature correlation and input size are considered to maximize the overall classification procedure of WS data correlation using the size and margin of the input and previously stored data. In both the differentiation and classification instances, the spatiotemporal features of data are extracted and analyzed using support vectors. The proposed SDC-SVM method can improve recognition accuracy, F1 score, and computing time for the varying WS inputs, classifications, and subjects.https://doi.org/10.1515/dema-2023-0261machine learningdata classificationfuzzy data correlationfeature extractionsvmwearable sensors94d0568q3262h20 |
| spellingShingle | Xu Pengru Zhou Junhui Kausar Nasreen Lin Chunlei Lu Qianqian Ghaderpour Ebrahim Pamucar Dragan Zadeh Ardashir M. A new machine learning approach based on spatial fuzzy data correlation for recognizing sports activities Demonstratio Mathematica machine learning data classification fuzzy data correlation feature extraction svm wearable sensors 94d05 68q32 62h20 |
| title | A new machine learning approach based on spatial fuzzy data correlation for recognizing sports activities |
| title_full | A new machine learning approach based on spatial fuzzy data correlation for recognizing sports activities |
| title_fullStr | A new machine learning approach based on spatial fuzzy data correlation for recognizing sports activities |
| title_full_unstemmed | A new machine learning approach based on spatial fuzzy data correlation for recognizing sports activities |
| title_short | A new machine learning approach based on spatial fuzzy data correlation for recognizing sports activities |
| title_sort | new machine learning approach based on spatial fuzzy data correlation for recognizing sports activities |
| topic | machine learning data classification fuzzy data correlation feature extraction svm wearable sensors 94d05 68q32 62h20 |
| url | https://doi.org/10.1515/dema-2023-0261 |
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