An AI-Based Exercise Prescription Recommendation System
The European Association of Preventive Cardiology Exercise Prescription in Everyday Practice and Rehabilitative Training (EXPERT) tool has been developed for digital training and decision support in cardiovascular disease patients in clinical practice. Exercise prescription recommendation systems fo...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/6/2661 |
| _version_ | 1797541137804689408 |
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| author | Hung-Kai Chen Fueng-Ho Chen Shien-Fong Lin |
| author_facet | Hung-Kai Chen Fueng-Ho Chen Shien-Fong Lin |
| author_sort | Hung-Kai Chen |
| collection | DOAJ |
| description | The European Association of Preventive Cardiology Exercise Prescription in Everyday Practice and Rehabilitative Training (EXPERT) tool has been developed for digital training and decision support in cardiovascular disease patients in clinical practice. Exercise prescription recommendation systems for sub-healthy people are essential to enhance this dominant group’s physical ability as well. This study aims to construct a guided exercise prescription system for sub-healthy groups using exercise community data to train an AI model. The system consists of six modules, including three-month suggested exercise mode (3m-SEM), predicted value of rest heart rate (rest HR) difference after following three-month suggested exercise mode (3m-PV), two-month suggested exercise mode (2m-SEM), predicted value of rest HR difference after following two-month suggested exercise mode (2m-PV), one-month suggested exercise mode (1m-SEM) and predicted value of rest HR difference after following one-month suggested exercise mode (1m-PV). A new user inputs gender, height, weight, age, and current rest HR value, and the above six modules will provide the user with a prescription. A four-layer neural network model is applied to construct the above six modules. The AI-enabled model produced 95.80%, 100.00%, and 95.00% testing accuracy in 1m-SEM, 2m-SEM, and 3m-SEM, respectively. It reached 3.15, 2.89, and 2.75 BPM testing mean absolute error in 1m-PV, 2m-PV, and 3m-PV. The developed system provides quantitative exercise prescriptions to guide the sub-healthy group to engage in effective exercise programs. |
| first_indexed | 2024-03-10T13:11:04Z |
| format | Article |
| id | doaj.art-b4511d3259fc4fc0aae0ab8456ae4cd1 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T13:11:04Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-b4511d3259fc4fc0aae0ab8456ae4cd12023-11-21T10:46:15ZengMDPI AGApplied Sciences2076-34172021-03-01116266110.3390/app11062661An AI-Based Exercise Prescription Recommendation SystemHung-Kai Chen0Fueng-Ho Chen1Shien-Fong Lin2Institute of Electrical and Computer Engineering, College of Electrical and Computer Engineering, National Chiao Tung University, 1001 University Road, Hsinchu 30010, TaiwanJoiiUp Technology Corporation, Hsinchu 30264, TaiwanInstitute of Biomedical Engineering, College of Electrical and Computer Engineering, National Chiao Tung University, 1001 University Road, Hsinchu 30010, TaiwanThe European Association of Preventive Cardiology Exercise Prescription in Everyday Practice and Rehabilitative Training (EXPERT) tool has been developed for digital training and decision support in cardiovascular disease patients in clinical practice. Exercise prescription recommendation systems for sub-healthy people are essential to enhance this dominant group’s physical ability as well. This study aims to construct a guided exercise prescription system for sub-healthy groups using exercise community data to train an AI model. The system consists of six modules, including three-month suggested exercise mode (3m-SEM), predicted value of rest heart rate (rest HR) difference after following three-month suggested exercise mode (3m-PV), two-month suggested exercise mode (2m-SEM), predicted value of rest HR difference after following two-month suggested exercise mode (2m-PV), one-month suggested exercise mode (1m-SEM) and predicted value of rest HR difference after following one-month suggested exercise mode (1m-PV). A new user inputs gender, height, weight, age, and current rest HR value, and the above six modules will provide the user with a prescription. A four-layer neural network model is applied to construct the above six modules. The AI-enabled model produced 95.80%, 100.00%, and 95.00% testing accuracy in 1m-SEM, 2m-SEM, and 3m-SEM, respectively. It reached 3.15, 2.89, and 2.75 BPM testing mean absolute error in 1m-PV, 2m-PV, and 3m-PV. The developed system provides quantitative exercise prescriptions to guide the sub-healthy group to engage in effective exercise programs.https://www.mdpi.com/2076-3417/11/6/2661exercise prescriptionsuggested exercise moderest heart rate |
| spellingShingle | Hung-Kai Chen Fueng-Ho Chen Shien-Fong Lin An AI-Based Exercise Prescription Recommendation System Applied Sciences exercise prescription suggested exercise mode rest heart rate |
| title | An AI-Based Exercise Prescription Recommendation System |
| title_full | An AI-Based Exercise Prescription Recommendation System |
| title_fullStr | An AI-Based Exercise Prescription Recommendation System |
| title_full_unstemmed | An AI-Based Exercise Prescription Recommendation System |
| title_short | An AI-Based Exercise Prescription Recommendation System |
| title_sort | ai based exercise prescription recommendation system |
| topic | exercise prescription suggested exercise mode rest heart rate |
| url | https://www.mdpi.com/2076-3417/11/6/2661 |
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