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

Applied Sciences

Hsiu-Tao Hsu¹  Wen-Hsien Ho²  Meng-Hua Lee³  Wen-Lan Wu³  Jing-Min Liang³ 

[1] Center for Physical and Health Education, National Sun Yat-Sen University, Kaohsiung 80424, Taiwan;Department of Healthcare Administration and Medical Informatics, Kaohsiung Medical University, Kaohsiung 80708, Taiwan;Department of Sports Medicine, Kaohsiung Medical University, Kaohsiung 80708, Taiwan;

关键词: FMS;    IMU sensor;    machine learning;    ordinal logistic regression;    confusion matrix;    kappa;   

DOI  :  10.3390/app11010096

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown