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Volume 11
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10.3390/app11010096
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Article

Development of an Automatic Functional Movement Screening System with Inertial Measurement Unit Sensors

by
Wen-Lan Wu
1,2,*,
Meng-Hua Lee
1,
Hsiu-Tao Hsu
3,
Wen-Hsien Ho
4 and
Jing-Min Liang
1
1
Department of Sports Medicine, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
2
Department of Medical Research, Kaohsiung Medical University Hospital, Kaohsiung 80708, Taiwan
3
Center for Physical and Health Education, National Sun Yat-Sen University, Kaohsiung 80424, Taiwan
4
Department of Healthcare Administration and Medical Informatics, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
*
Author to whom correspondence should be addressed.
Appl. Sci. 2021, 11(1), 96; https://doi.org/10.3390/app11010096
Submission received: 18 November 2020 / Revised: 19 December 2020 / Accepted: 21 December 2020 / Published: 24 December 2020
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Abstract

Background: In this study, an automatic scoring system for the functional movement screen (FMS) was developed. Methods: Thirty healthy adults fitted with full-body inertial measurement unit sensors completed six FMS exercises. The system recorded kinematics data, and a professional athletic trainer graded each participant. To reduce the number of input variables for the predictive model, ordinal logistic regression was used for subset feature selection. The ensemble learning algorithm AdaBoost.M1 was used to construct classifiers. Accuracy and F score were used for classification model evaluation. The consistency between automatic and manual scoring was assessed using a weighted kappa statistic. Results: When all the features were used, the predict model presented moderate to high accuracy, with kappa values between fair to very good agreement. After feature selection, model accuracy decreased about 10%, with kappa values between poor to moderate agreement. Conclusions: The results indicate that higher prediction accuracy was achieved using the full feature set compared with using the reduced feature set.
Keywords: FMS; IMU sensor; machine learning; ordinal logistic regression; confusion matrix; kappa FMS; IMU sensor; machine learning; ordinal logistic regression; confusion matrix; kappa

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MDPI and ACS Style

Wu, W.-L.; Lee, M.-H.; Hsu, H.-T.; Ho, W.-H.; Liang, J.-M. Development of an Automatic Functional Movement Screening System with Inertial Measurement Unit Sensors. Appl. Sci. 2021, 11, 96. https://doi.org/10.3390/app11010096

AMA Style

Wu W-L, Lee M-H, Hsu H-T, Ho W-H, Liang J-M. Development of an Automatic Functional Movement Screening System with Inertial Measurement Unit Sensors. Applied Sciences. 2021; 11(1):96. https://doi.org/10.3390/app11010096

Chicago/Turabian Style

Wu, Wen-Lan, Meng-Hua Lee, Hsiu-Tao Hsu, Wen-Hsien Ho, and Jing-Min Liang. 2021. "Development of an Automatic Functional Movement Screening System with Inertial Measurement Unit Sensors" Applied Sciences 11, no. 1: 96. https://doi.org/10.3390/app11010096

APA Style

Wu, W.-L., Lee, M.-H., Hsu, H.-T., Ho, W.-H., & Liang, J.-M. (2021). Development of an Automatic Functional Movement Screening System with Inertial Measurement Unit Sensors. Applied Sciences, 11(1), 96. https://doi.org/10.3390/app11010096

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