Intelligent System of Squat Analysis Exercise to Prevent Back Injuries. Rosero-Montalvo, P., Dibujes, A., Vásquez-Ayala, C., Umaquinga-Criollo, A., Michilena, J., Suaréz, L., Flores, S., & Jaramillo, D. Volume 884 , 2019.
abstract   bibtex   
© 2019, Springer Nature Switzerland AG. The sports ergonomics study allows a bio-mechanical analysis in order to evaluate the impact produced by different muscle conditioning exercises such as the squat. This exercise, if carried out in an erroneous way, it can cause lumbar injuries. The present electronic system acquire the data of the Smith bar and the back by means of accelerometer sensors. This is done in order to implement an intelligent algorithm that allows to recognize if the athlete performs the exercise properly. For this, a stage of prototypes selection and a comparison of classification algorithms (CA) is carried out. Finally, a quantitative measure of equilibrium between both criteria is established for its proper selection. As a result, the k-Nearest Neighbors algorithm with k = 5 achieves a 96% performance and a 50% training matrix reduction.
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 title = {Intelligent System of Squat Analysis Exercise to Prevent Back Injuries},
 type = {book},
 year = {2019},
 source = {Advances in Intelligent Systems and Computing},
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 keywords = {Back injuries,Embedded systems,Intelligent systems,Squat analysis},
 volume = {884},
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 created = {2018-11-07T23:59:00.000Z},
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 abstract = {© 2019, Springer Nature Switzerland AG. The sports ergonomics study allows a bio-mechanical analysis in order to evaluate the impact produced by different muscle conditioning exercises such as the squat. This exercise, if carried out in an erroneous way, it can cause lumbar injuries. The present electronic system acquire the data of the Smith bar and the back by means of accelerometer sensors. This is done in order to implement an intelligent algorithm that allows to recognize if the athlete performs the exercise properly. For this, a stage of prototypes selection and a comparison of classification algorithms (CA) is carried out. Finally, a quantitative measure of equilibrium between both criteria is established for its proper selection. As a result, the k-Nearest Neighbors algorithm with k = 5 achieves a 96% performance and a 50% training matrix reduction.},
 bibtype = {book},
 author = {Rosero-Montalvo, P.D. and Dibujes, A. and Vásquez-Ayala, C. and Umaquinga-Criollo, A. and Michilena, J.R. and Suaréz, L. and Flores, S. and Jaramillo, D.}
}

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