This study examined the goodness-of-fit of using a sigmoid function to characterize time-series angular displacement trajectories during two-handed anterior lifting. Twenty-six participants performed two-handed anterior lifting with a low (4.5 kg) vs. high (22.7 kg) load at floor vs. knee lifting height. A sigmoid function with three parameters was fit to the torso-thigh included angle, knee flexion-extension (F-E), and ankle F-E angles in the sagittal plane obtained from body-worn inertial sensors. Mean ± SD RMSE between measured vs. fitted trajectories were 3.6 ± 2.9°, 3.9 ± 4.2°, and 2.7 ± 2.8° for the torso-thigh included angle, knee F-E, and ankle F-E angles, respectively. Findings suggest that the sigmoid function adequately describes the trajectory shape of two-handed lifting kinematics. Functional representations facilitate data aggregation and feature extraction in large time-series datasets encountered in inertial-based motion analysis and machine learning applications.
Keywords: Curve-fitting; Functional data; Lifting kinematics; Wearable sensing.