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Machine learning approach for fatigue estimation in sit-to-stand exercise

Andrés Aguirre, Maria Jose Pinto Bernal (UGent) , Carlos A. Cifuentes, Oscar Perdomo, Camilo A. R. Díaz and Marcela Múnera
(2021) SENSORS. 21(15).
Author
Organization
Abstract
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.
Keywords
Electrical and Electronic Engineering, Biochemistry, Instrumentation, Atomic and Molecular Physics, and Optics, Analytical Chemistry, fatigue estimation, Kinect, machine learning, physical exercise, physical rehabilitation, sit-to-stand, CORONARY-HEART-DISEASE, TIME-FREQUENCY METHODS, PHYSICAL-ACTIVITY, PERCEIVED EXERTION, MUSCLE FATIGUE, PULMONARY REHABILITATION, MUSCULOSKELETAL FITNESS, RELATIVE INTENSITY, AEROBIC CAPACITY, OXYGEN-UPTAKE

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Citation

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MLA
Aguirre, Andrés, et al. “Machine Learning Approach for Fatigue Estimation in Sit-to-Stand Exercise.” SENSORS, vol. 21, no. 15, 2021, doi:10.3390/s21155006.
APA
Aguirre, A., Pinto Bernal, M. J., Cifuentes, C. A., Perdomo, O., Díaz, C. A. R., & Múnera, M. (2021). Machine learning approach for fatigue estimation in sit-to-stand exercise. SENSORS, 21(15). https://doi.org/10.3390/s21155006
Chicago author-date
Aguirre, Andrés, Maria Jose Pinto Bernal, Carlos A. Cifuentes, Oscar Perdomo, Camilo A. R. Díaz, and Marcela Múnera. 2021. “Machine Learning Approach for Fatigue Estimation in Sit-to-Stand Exercise.” SENSORS 21 (15). https://doi.org/10.3390/s21155006.
Chicago author-date (all authors)
Aguirre, Andrés, Maria Jose Pinto Bernal, Carlos A. Cifuentes, Oscar Perdomo, Camilo A. R. Díaz, and Marcela Múnera. 2021. “Machine Learning Approach for Fatigue Estimation in Sit-to-Stand Exercise.” SENSORS 21 (15). doi:10.3390/s21155006.
Vancouver
1.
Aguirre A, Pinto Bernal MJ, Cifuentes CA, Perdomo O, Díaz CAR, Múnera M. Machine learning approach for fatigue estimation in sit-to-stand exercise. SENSORS. 2021;21(15).
IEEE
[1]
A. Aguirre, M. J. Pinto Bernal, C. A. Cifuentes, O. Perdomo, C. A. R. Díaz, and M. Múnera, “Machine learning approach for fatigue estimation in sit-to-stand exercise,” SENSORS, vol. 21, no. 15, 2021.
@article{8745837,
  abstract     = {{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.}},
  articleno    = {{5006}},
  author       = {{Aguirre, Andrés and Pinto Bernal, Maria Jose and Cifuentes, Carlos A. and Perdomo, Oscar and Díaz, Camilo A. R. and Múnera, Marcela}},
  issn         = {{1424-8220}},
  journal      = {{SENSORS}},
  keywords     = {{Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics,and Optics,Analytical Chemistry,fatigue estimation,Kinect,machine learning,physical exercise,physical rehabilitation,sit-to-stand,CORONARY-HEART-DISEASE,TIME-FREQUENCY METHODS,PHYSICAL-ACTIVITY,PERCEIVED EXERTION,MUSCLE FATIGUE,PULMONARY REHABILITATION,MUSCULOSKELETAL FITNESS,RELATIVE INTENSITY,AEROBIC CAPACITY,OXYGEN-UPTAKE}},
  language     = {{eng}},
  number       = {{15}},
  pages        = {{31}},
  title        = {{Machine learning approach for fatigue estimation in sit-to-stand exercise}},
  url          = {{http://doi.org/10.3390/s21155006}},
  volume       = {{21}},
  year         = {{2021}},
}
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