In recent decades, although the research on gait recognition of lower limb exoskeleton robot has been widely developed, there are still limitations in rehabilitation training and clinical practice. The emergence of interactive information fusion technology provides a new research idea for the solution of this problem, and it is also the development trend in the future. In order to better explore the issue, this paper summarizes gait recognition based on interactive information fusion of lower limb exoskeleton robots. This review introduces the current research status, methods, and directions for information acquisition, interaction, fusion, and gait recognition of exoskeleton robots. The content involves the research progress of information acquisition methods, sensor placements, target groups, lower limb sports biomechanics, interactive information fusion, and gait recognition model. Finally, the current challenges, possible solutions, and promising prospects are analysed and discussed, which provides a useful reference resource for the study of interactive information fusion and gait recognition of rehabilitation exoskeleton robots.
The dynamics and vibration characteristics of a 3-UPU parallel mechanism isolator are investigated by theoretical modeling, numerical simulation, and experimental study. The system consists of two platforms, three linear motors, and the Hook hinges. Firstly, the dynamical mathematical model of this vibration isolator is innovatively established and solved by using the discrete-time transfer matrix method of the multibody system (MS-DT-TMM). According to the modeling principle, the transfer matrix of each component is derived, including the upper and lower platforms, Hooke hinges, and linear motors. Then, the dynamical equation of the overall system is obtained by multiplying all transfer matrices. Secondly, the solution of equation is calculated through the setting of boundary conditions. The numerical simulation is carried out according to the known parameters. The dynamical and vibration analysis of the isolation platform is performed, involving the displacement and force characteristics of the branches. Furthermore, in view of the fact that the Hooke hinges and linear motors are simplified as spatial elastic hinges in modeling. The vibration suppression effect caused by adjusting spring stiffness and damping coefficient is discussed. The simulation results verified the correctness of the MS-DT-TMM method through the comparison with ADAMS simulation results. Finally, the prototype of the vibration isolator is constructed and assembled, and the vibration experiment is conducted. By testing the responses of the isolation device mounted on the vibration table, the natural frequency of the isolator is obtained. The purpose of this experiment is to avoid resonance when it is applied as a vibration isolator in the future. This paper provides a theoretical basis for the later vibration research and control scheme design of the 3-UPU parallel vibration isolation platform.
Human activity recognition (HAR) has attracted considerable research attention in the past decade with the development of wearable sensor technology and deep learning algorithms. However, most of the existing HAR methods ignored the spatial relationship of features, which may lead to recognition errors. In this paper, a novel model based on a modified capsule network (MCN) is proposed to accurately recognize various human activities. This novel model is composed of a convolution block and a capsule block, which can achieve end-to-end intelligent recognition. In the meantime, the spatial information among features is preserved through a dynamic routing process. To validate the effectiveness of the model, a human activity dataset is constructed by placing an inertial measurement unit (IMU) on the calf of the volunteers to collect their activity data in daily life, including walking, jogging, upstairs, downstairs, up-ramps, and down-ramps. The recognition accuracy of this novel approach can reach 96.08%, which performs better than the convolutional neural network (CNN) with an accuracy of 91.62%. In addition, it is evaluated on two public datasets named WISDM and UCI-HAR, and the accuracies achieve 98.21% and 95.28%, respectively, which presents higher accuracy than the reported results obtained from benchmark algorithms like CNN. The experimental results show that the proposed model has better activity detection capability and achieves outstanding performance for HAR.
In recent years, robots based on fixed base cannot meet the increasingly prominent application needs such as in service and industry. Compared with traditional robots, the mobile flexible manipulator has wider workspace and more flexible characteristics. Therefore, it is gradually becoming the focus of attention. In this paper, the dynamic modeling, analysis and experimental research are investigated by employed MS-DT-TMM (Discrete Time Transfer Matrix Method for Multibody System) for a wheeled mobile robot system with a flexible manipulator. Firstly, the discrete-time transfer matrix method and its solution process are overviewed. Then, the 3-joint flexible manipulator mounted on the wheeled mobile robot is divided into 8 components. According to the idea of modeling method, the system dynamic model is integrated by using the transfer matrix equations of 8 components. Moreover, based on the existing experimental conditions, the dynamic simulation and experimental research of single degree of freedom planar flexible manipulator are carried out. The data results validate the correctness and feasibility of the proposed model. Finally, the numerical simulation research of whole mobile flexible robot is completed. The effectiveness of the model is verified by analyzing the data of wheel forces, links and joints motion, the displacements of the end effector and so on. Further, the comparison of the end errors of the mobile robot carrying rigid link and flexible link in the simulation results demonstrates the necessity of modeling the flexible component in the system.
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