Splining raw kinematic data of standing-bending-lifting movement

Spline functions and digital filtering are two numerical methods normally used by the researchers in the field of biomechanics to perform data smoothing and time differentiation. The objective of this study is to perform data smoothing on the raw kinematic data of a subject performing standing-bending-lifting activity using spline function, and to calculate time differentiation from the kinematic data. Raw data on standing-bending-lifting movement was recorded in Biomechanics Lab, UniMAP. Next, quintic spline function was used to smooth the data and calculate the angular velocity and angular acceleration at L5, L3, L1, MAI and T2. When calculated for one trial, the error estimation between smooth and raw data at L5, L3, L1, MAI, and T2 are 0.008°, 0.003°, 0.003°, 0.004°, and 0.008°, respectively. When calculating for five trials, the average error estimation between smooth and raw data at L5, L3, L1, MAI, and T2 are 0.007°, 0.009°, 0.011°, 0.012°, and 0.012°, respectively. The result shows that the quintic spline is able to produce satisfactory output in data smoothing.