Sit-stand movement is one of the most common movement behaviors of the human body. The knee joint is the main bearing joint of this movement. Thus, the dynamic analysis of knee joint during this movement has deeply positive influences. According to the principle of moment balance, the dynamics of the knee joint during the movement were analyzed. Furthermore, combined with the data obtained from optical motion capture and six-dimensional ground reaction force test, the curve of knee joint torque was calculated. To verify the accuracy of the analysis of dynamic, the human body model was established, the polynomial equations of angle and angular velocity were fitted according to the experimental data, and the knee joint simulation of the movement was carried out. The result revealed that in terms of range and trend, the theoretical data and simulation data were consistent. The relationship between knee joint torque and ground reaction force was revealed based on the variation law of knee joint torque. During the sit-stand movement, the knee joint torque and the ground reaction force were directly proportional to each other, and the ratio was 5 to 6. In the standing process, the acceleration first increased and then decreased and finally increased in reverse, and the maximum knee torque occurred at an angle of about 140°. In the sitting process, the torque was maximized in the initial stage. The results of the dynamics analysis of knee joint during sit-stand movement are beneficial to the optimal design and force feedback control of seated rehabilitation aids, and can provide theoretical guidance for knee rehabilitation training.
Traditional gait analysis systems are typically complex to operate, lack portability, and involve high equipment costs. This study aims to establish a musculoskeletal dynamics calculation process driven by Azure Kinect. Building upon the full-body model of the Anybody musculoskeletal simulation software and incorporating a foot-ground contact model, the study utilized Azure Kinect-driven skeletal data from depth videos of 10 participants. The in-depth videos were prepossessed to extract keypoint of the participants, which were then adopted as inputs for the musculoskeletal model to compute lower limb joint angles, joint contact forces, and ground reaction forces. To validate the Azure Kinect computational model, the calculated results were compared with kinematic and kinetic data obtained using the traditional Vicon system. The forces in the lower limb joints and the ground reaction forces were normalized by dividing them by the body weight. The lower limb joint angle curves showed a strong correlation with Vicon results (mean ρ values: 0.78 ~ 0.92) but with root mean square errors as high as 5.66°. For lower limb joint force prediction, the model exhibited root mean square errors ranging from 0.44 to 0.68, while ground reaction force root mean square errors ranged from 0.01 to 0.09. The established musculoskeletal dynamics model based on Azure Kinect shows good prediction capabilities for lower limb joint forces and vertical ground reaction forces, but some errors remain in predicting lower limb joint angles.