Running and Cycling Induced Fatigue on Wrapper vs. BLR Feature Selection for IBk Classification

International Conference on Recent Advances in Industrial Engineering and Manufacturing

工业技术（总论）

Tang, S.^1 ; Loh, W.P.^1 ; Lin, C.F.^2

School of Mechanical Engineering, Engineering Campus, Universiti Sains Malaysia, Nibong Tebal, Penang

14300, Malaysia^1

Department of Physical Therapy, National Cheng Kung University, Tainan City, Taiwan^2

关键词: Accidental injuries;    Binary logistic regression;    Classification accuracy;    Classification tests;    Cycle frequencies;    Cycling frequencies;    Triaxial accelerometer;    Wrapper approach;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/530/1/012060/pdf
DOI  :  10.1088/1757-899X/530/1/012060

学科分类：工业工程学

来源: IOP

【 摘 要 】

Running and cycling fatigue causes muscle pains, cramps and accidental injuries. Previous studies had considered the importance of tri-axial accelerometer to detect fatigue motion in stability, balance and postural deviation aspects. While tri-axial accelerometer is important, the capability to predict running and cycling fatigue from the biomechanical attributes were unclear. Therefore, the study aims to (i) compare the featured attributes selected from wrapper approach and Binary Logistic Regression (BLR) on running and cycling datasets and (ii) perform IBk classification accuracy comparison on the feature selection attributes. Public running, experimental running and cycling induced fatigue datasets were employed to test the analysis. The most significant attributes identified in the public running was RMS-ML, followed by Range-ML and the cycle frequency in experimental running and cycling respectively. On 10 folds cross-validation classification test using the IBk algorithm in WEKA, accuracies for experimental running and cycling datasets were 93.1% and 90.5% from wrapper method, 65.6% and 76.2% from BLR respectively. Wrapper method performs better than BLR in data overfitting phenomenon. Findings reveal that the mediolateral variation at body trunk motion plays a major impact to predict fatigue running but fatigue cycling shows cycling frequency as the main attribute in fatigue cycling prediction.