Hsieh Hong-Jung, Lin Hsiu-Chen, Lu Hsuan-Lun, Chen Ting-Yi, Lu Tung-Wu
Institute of Biomedical Engineering, National Taiwan University, Taiwan, ROC; Department of Mechanics Engineering, Kao Yuan University, Taiwan, ROC.
Institute of Biomedical Engineering, National Taiwan University, Taiwan, ROC; School of Physical Therapy, China Medical University, Taiwan, ROC.
Gait Posture. 2016 Mar;45:217-23. doi: 10.1016/j.gaitpost.2016.01.018. Epub 2016 Jan 28.
Instrumented treadmills (ITs) are used to measure reaction forces (RF) and center of pressure (COP) movements for gait and balance assessment. Regular in situ calibration is essential to ensure their accuracy and to identify conditions when a factory re-calibration is needed. The current study aimed to develop and calibrate in situ an IT using a portable, precision-controlled calibration device with an artificial neural network (ANN)-based correction method. The calibration device was used to apply static and dynamic calibrating loads to the surface of the IT at 189 and 25 grid-points, respectively, at four belt speeds (0, 4, 6 and 8 km/h) without the need of a preset template. Part of the applied and measured RF and COP were used to train a threelayered, back-propagation ANN model while the rest of the data were used to evaluate the performance of the ANN. The percent errors of Fz and errors of the Px and Py were significantly decreased from a maximum of -1.15%, -1.64 mm and -0.73 mm to 0.02%, 0.02 mm and 0.03 mm during static calibration, respectively. During dynamic calibration, the corresponding values were decreasing from -3.65%, 2.58 mm and -4.92 mm to 0.30%, -0.14 mm and -0.47 mm, respectively. The results suggest that the calibration device and associated ANN will be useful for correcting measurement errors in vertical loads and COP for ITs.
