The analysis of infrared high-speed motion capture system on motion aesthetics of aerobics athletes under biomechanics analysis

Abstract

This paper uses an infrared high-speed motion capture system based on deep learning to analyze difficult movements, which helps aerobics athletes master difficult movements more accurately. Firstly, changes in joint angle, speed of movement, and ground pressure are used to analyze the impact and role of motion fluency and completion based on a biomechanical perspective. Moreover, based on the existing infrared high-speed motion capture systems, the Restricted Boltzmann Machine (RBM) model is introduced to construct an unsupervised similarity framework model. Next, the motion data is reorganized based on three-dimensional information to adapt to the model’s input. Then, the framework performs similar frame matching to obtain a set of candidate frames that can be used as motion graph nodes. After the infrared high-speed motion capture system and inertial sensors are simultaneously applied to subjects, the multi-correlation coefficients (CMC) values of the hip, knee, and ankle angles are 0.94 ± 0.06, 0.98 ± 0.01, and 0.87 ± 0.09, respectively. The two systems show a high degree of correlation in the measurement results, and the knee joint is the most significant correlation. Finally, a motion graph is constructed to control its trajectory and adjust its motion pattern. The infrared high-speed motion capture system optimized for deep learning can extract features from human bone data and capture motion more accurately, helping trainers to fully understand difficult movements.