Kinematic Analysis of Intra-Limb Joint Symmetry via Multisensor Fusion

Abstract

The symmetry of rowing movement is essential for injury prevention and motion effectiveness. Much effort has been dedicated to developing low-cost and intuitive methods for assessing kayaking athletes’ posture. This study proposes a design of motion capture and kinematic analysis based on inertial sensors under real on-water conditions. Athletes with different proficient level are recruited to participate in the experiments. Motion data is collected by self-made devices. The indirect Kalman filter algorithm is used to fuse multiple sensor data after calibrations. The whole body posture and multi-joint coordination can be calculated accordingly. Wavelet scattering is used for automatic feature extraction, and various machine learning algorithms are used for phase recognition. Symmetry analysis is achieved based on the results of phase segmentation in one paddling stroke cycle. Postural control strategy of symmetry is assessed from the perspective of joint angles. The results show that the parameters, which represent the orientation of inertia’s principal axis of the cyclogram based on joint angle can reflect the symmetric difference between elite and novice.