Based on the biomechanical simulation curve of OpenSim, an open source software of biomechanical model, a spherical exoskeleton parallel mechanism with two degrees of freedom for hip joint is proposed in this paper for the rehabilitation therapy of patients with impaired leg motor function or elderly people with walking dysfunction. Firstly, the parallel mechanism is modeled and the position inverse solution of the parallel mechanism is obtained using inverse kinematics analysis. The velocity analysis expression of the mechanism is derived by deriving the inverse kinematics solution. The model is imported into the mechanical system dynamics analysis software ADAMS and matrix processing analysis software MATLAB to carry out simulation experiments. The correctness of the velocity analysis is verified by comparing the velocity simulation results of the two methods. Then, three singular types of the mechanism are analyzed according to the obtained Jacobian matrix. According to the inverse solution of the mechanism, the reachable workspace of the mechanism is obtained by programming in MATLAB with given mechanism parameters and restriction conditions. Finally, the prototype platform is built. The experimental results show that the exoskeleton hip joint using this parallel mechanism can satisfy the requirement of rotation angle of human hip joint movement, but also can be good to assist patients with leg flexion-extension movement and adduction-abduction movement, and it is helpful to carry out corresponding rehabilitation training. It also has theoretical significance and application value for the research work of human hip exoskeleton parallel mechanism.
Early screening based on computed tomography (CT) pulmonary nodule detection is an important means to reduce lung cancer mortality, and in recent years three dimensional convolutional neural network (3D CNN) has achieved success and continuous development in the field of lung nodule detection. We proposed a pulmonary nodule detection algorithm by using 3D CNN based on a multi-scale attention mechanism. Aiming at the characteristics of different sizes and shapes of lung nodules, we designed a multi-scale feature extraction module to extract the corresponding features of different scales. Through the attention module, the correlation information between the features was mined from both spatial and channel perspectives to strengthen the features. The extracted features entered into a pyramid-similar fusion mechanism, so that the features would contain both deep semantic information and shallow location information, which is more conducive to target positioning and bounding box regression. On representative LUNA16 datasets, compared with other advanced methods, this method significantly improved the detection sensitivity, which can provide theoretical reference for clinical medicine.