Machine Learning Approach for Fatigue Estimation in Sit-to-Stand Exercise
Physical exercise (PE) has become an essential tool for different rehabilitation programs. High-intensity exercises (HIEs) have been demonstrated to provide better results in general health conditions, compared with low and moderate-intensity exercises. In this context, monitoring of a patients’ con...
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MDPI AG
2021-07-01
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| Online Access: | https://www.mdpi.com/1424-8220/21/15/5006 |
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| author | Andrés Aguirre Maria J. Pinto Carlos A. Cifuentes Oscar Perdomo Camilo A. R. Díaz Marcela Múnera |
| author_facet | Andrés Aguirre Maria J. Pinto Carlos A. Cifuentes Oscar Perdomo Camilo A. R. Díaz Marcela Múnera |
| author_sort | Andrés Aguirre |
| collection | DOAJ |
| description | Physical exercise (PE) has become an essential tool for different rehabilitation programs. High-intensity exercises (HIEs) have been demonstrated to provide better results in general health conditions, compared with low and moderate-intensity exercises. In this context, monitoring of a patients’ condition is essential to avoid extreme fatigue conditions, which may cause physical and physiological complications. Different methods have been proposed for fatigue estimation, such as: monitoring the subject’s physiological parameters and subjective scales. However, there is still a need for practical procedures that provide an objective estimation, especially for HIEs. In this work, considering that the sit-to-stand (STS) exercise is one of the most implemented in physical rehabilitation, a computational model for estimating fatigue during this exercise is proposed. A study with 60 healthy volunteers was carried out to obtain a data set to develop and evaluate the proposed model. According to the literature, this model estimates three fatigue conditions (low, moderate, and high) by monitoring 32 STS kinematic features and the heart rate from a set of ambulatory sensors (Kinect and Zephyr sensors). Results show that a random forest model composed of 60 sub-classifiers presented an accuracy of 82.5% in the classification task. Moreover, results suggest that the movement of the upper body part is the most relevant feature for fatigue estimation. Movements of the lower body and the heart rate also contribute to essential information for identifying the fatigue condition. This work presents a promising tool for physical rehabilitation. |
| first_indexed | 2024-03-10T09:09:40Z |
| format | Article |
| id | doaj.art-f3d3b2dd252a46cb9b9eb3f30d1f2f25 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T09:09:40Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-f3d3b2dd252a46cb9b9eb3f30d1f2f252023-11-22T06:09:02ZengMDPI AGSensors1424-82202021-07-012115500610.3390/s21155006Machine Learning Approach for Fatigue Estimation in Sit-to-Stand ExerciseAndrés Aguirre0Maria J. Pinto1Carlos A. Cifuentes2Oscar Perdomo3Camilo A. R. Díaz4Marcela Múnera5Department of Biomedical Engineering, Colombian School of Engineering Julio Garavito, Bogotá 111166, ColombiaDepartment of Biomedical Engineering, Colombian School of Engineering Julio Garavito, Bogotá 111166, ColombiaDepartment of Biomedical Engineering, Colombian School of Engineering Julio Garavito, Bogotá 111166, ColombiaSchool of Medicine and Health Sciences, Universidad del Rosario, Bogotá 111711, ColombiaElectrical Engineering Department, Federal University of Espirito Santo, Vitoria 29075-910, BrazilDepartment of Biomedical Engineering, Colombian School of Engineering Julio Garavito, Bogotá 111166, ColombiaPhysical exercise (PE) has become an essential tool for different rehabilitation programs. High-intensity exercises (HIEs) have been demonstrated to provide better results in general health conditions, compared with low and moderate-intensity exercises. In this context, monitoring of a patients’ condition is essential to avoid extreme fatigue conditions, which may cause physical and physiological complications. Different methods have been proposed for fatigue estimation, such as: monitoring the subject’s physiological parameters and subjective scales. However, there is still a need for practical procedures that provide an objective estimation, especially for HIEs. In this work, considering that the sit-to-stand (STS) exercise is one of the most implemented in physical rehabilitation, a computational model for estimating fatigue during this exercise is proposed. A study with 60 healthy volunteers was carried out to obtain a data set to develop and evaluate the proposed model. According to the literature, this model estimates three fatigue conditions (low, moderate, and high) by monitoring 32 STS kinematic features and the heart rate from a set of ambulatory sensors (Kinect and Zephyr sensors). Results show that a random forest model composed of 60 sub-classifiers presented an accuracy of 82.5% in the classification task. Moreover, results suggest that the movement of the upper body part is the most relevant feature for fatigue estimation. Movements of the lower body and the heart rate also contribute to essential information for identifying the fatigue condition. This work presents a promising tool for physical rehabilitation.https://www.mdpi.com/1424-8220/21/15/5006fatigue estimationKinectmachine learningphysical exercisephysical rehabilitationsit-to-stand |
| spellingShingle | Andrés Aguirre Maria J. Pinto Carlos A. Cifuentes Oscar Perdomo Camilo A. R. Díaz Marcela Múnera Machine Learning Approach for Fatigue Estimation in Sit-to-Stand Exercise Sensors fatigue estimation Kinect machine learning physical exercise physical rehabilitation sit-to-stand |
| title | Machine Learning Approach for Fatigue Estimation in Sit-to-Stand Exercise |
| title_full | Machine Learning Approach for Fatigue Estimation in Sit-to-Stand Exercise |
| title_fullStr | Machine Learning Approach for Fatigue Estimation in Sit-to-Stand Exercise |
| title_full_unstemmed | Machine Learning Approach for Fatigue Estimation in Sit-to-Stand Exercise |
| title_short | Machine Learning Approach for Fatigue Estimation in Sit-to-Stand Exercise |
| title_sort | machine learning approach for fatigue estimation in sit to stand exercise |
| topic | fatigue estimation Kinect machine learning physical exercise physical rehabilitation sit-to-stand |
| url | https://www.mdpi.com/1424-8220/21/15/5006 |
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