Wearable Sensor-Based Exercise Monitoring System for Higher Education Students Using a Multi-Attribute Fuzzy Evaluation Model
Exercise and Physical Activity are important factors to improve the student's health and academic status. Student exercise should be continuously monitored to eliminate risk factors and health issues. The previous monitoring system faced difficulties while handling the vast amount of data obtai...
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IEEE
2024-01-01
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| author | Shiping Yu Xiaowei Peng |
| author_facet | Shiping Yu Xiaowei Peng |
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| description | Exercise and Physical Activity are important factors to improve the student's health and academic status. Student exercise should be continuously monitored to eliminate risk factors and health issues. The previous monitoring system faced difficulties while handling the vast amount of data obtained from multiple sensors because it was affected by uncertainty and noise issues. The research difficulties are addressed with the help of the Multi-Attribute Fuzzy Evaluation Model (MAFEM), which monitors student's health using sensor data. The MAFEM approach uses the fuzzy set and fuzzy logic to derive the relationship between the features. In addition, the method uses preprocessing, fuzzification, defuzzification and rule evaluation processes. These steps are adjusted according to the threshold value that maximizes the personalization and holistic assessment efficiency because the system uses multiple attributes. During the analysis, MM-Fit dataset information is utilized to evaluate the system efficiency in which the system ensures the minimum computation complexity <inline-formula> <tex-math notation="LaTeX">$O\left ({{ r.m.n }}\right)$ </tex-math></inline-formula> and minimum latency value <inline-formula> <tex-math notation="LaTeX">$\left ({{ \approx 70mAh }}\right)$ </tex-math></inline-formula>.. In addition, the accuracy metrics are also applied to evaluate the system's effectiveness, with 97.11% precision, 0.23 RMSE and 0.26 MSE values. |
| format | Article |
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| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
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| spelling | doaj-art-cf127081827740aebd12a3013091adcd2025-01-23T00:00:26ZengIEEEIEEE Access2169-35362024-01-011217741217742610.1109/ACCESS.2024.349488510749832Wearable Sensor-Based Exercise Monitoring System for Higher Education Students Using a Multi-Attribute Fuzzy Evaluation ModelShiping Yu0Xiaowei Peng1https://orcid.org/0009-0003-0987-4935School of Physical Education, Wuhan Sports University, Wuhan, ChinaSchool of Physical Education, Wuhan Sports University, Wuhan, ChinaExercise and Physical Activity are important factors to improve the student's health and academic status. Student exercise should be continuously monitored to eliminate risk factors and health issues. The previous monitoring system faced difficulties while handling the vast amount of data obtained from multiple sensors because it was affected by uncertainty and noise issues. The research difficulties are addressed with the help of the Multi-Attribute Fuzzy Evaluation Model (MAFEM), which monitors student's health using sensor data. The MAFEM approach uses the fuzzy set and fuzzy logic to derive the relationship between the features. In addition, the method uses preprocessing, fuzzification, defuzzification and rule evaluation processes. These steps are adjusted according to the threshold value that maximizes the personalization and holistic assessment efficiency because the system uses multiple attributes. During the analysis, MM-Fit dataset information is utilized to evaluate the system efficiency in which the system ensures the minimum computation complexity <inline-formula> <tex-math notation="LaTeX">$O\left ({{ r.m.n }}\right)$ </tex-math></inline-formula> and minimum latency value <inline-formula> <tex-math notation="LaTeX">$\left ({{ \approx 70mAh }}\right)$ </tex-math></inline-formula>.. In addition, the accuracy metrics are also applied to evaluate the system's effectiveness, with 97.11% precision, 0.23 RMSE and 0.26 MSE values.https://ieeexplore.ieee.org/document/10749832/Defuzzificationfuzzificationhigher education studentsMM-Fit datasetcomputation complexity and latencymultiattribute |
| spellingShingle | Shiping Yu Xiaowei Peng Wearable Sensor-Based Exercise Monitoring System for Higher Education Students Using a Multi-Attribute Fuzzy Evaluation Model IEEE Access Defuzzification fuzzification higher education students MM-Fit dataset computation complexity and latency multiattribute |
| title | Wearable Sensor-Based Exercise Monitoring System for Higher Education Students Using a Multi-Attribute Fuzzy Evaluation Model |
| title_full | Wearable Sensor-Based Exercise Monitoring System for Higher Education Students Using a Multi-Attribute Fuzzy Evaluation Model |
| title_fullStr | Wearable Sensor-Based Exercise Monitoring System for Higher Education Students Using a Multi-Attribute Fuzzy Evaluation Model |
| title_full_unstemmed | Wearable Sensor-Based Exercise Monitoring System for Higher Education Students Using a Multi-Attribute Fuzzy Evaluation Model |
| title_short | Wearable Sensor-Based Exercise Monitoring System for Higher Education Students Using a Multi-Attribute Fuzzy Evaluation Model |
| title_sort | wearable sensor based exercise monitoring system for higher education students using a multi attribute fuzzy evaluation model |
| topic | Defuzzification fuzzification higher education students MM-Fit dataset computation complexity and latency multiattribute |
| url | https://ieeexplore.ieee.org/document/10749832/ |
| work_keys_str_mv | AT shipingyu wearablesensorbasedexercisemonitoringsystemforhighereducationstudentsusingamultiattributefuzzyevaluationmodel AT xiaoweipeng wearablesensorbasedexercisemonitoringsystemforhighereducationstudentsusingamultiattributefuzzyevaluationmodel |