Nagoya, Many physical medicine and rehabilitation strategies have been developed for physically disabled people. Most of them have been developed by the physical therapists who assess anatomical and physiological states of the patients. We can design a better rehabilitation method with application of robot technologies, since we can achieve complex motional trajectories in smooth and accurate manner, realize creatively active and passive guidance of patient's limb motion according to the patient's state, and even load and unload on specific muscles. We have developed a sequential design method of lower limb rehabilitation trajectory and external force acting on foot based on a 2D musculo-skeletal model of lower-limb. In this paper, we extend the previously developed method to a 3D musculo-skeletal model for 3D training trajectory and external force design. We introduce spline curves for both smooth position and force tracking trajectories. The structural parameters of the spline curves are explored by applying genetic algorithm. The usefulness of the proposed method is confirmed through simulation results and the comparison with a conventional training trajectory.
We introduce a method for lower-limb physical rehabilitation by means of a robot that applies preliminary defined forces to a patient's foot while moving it on a preliminary defined trajectory. We developed a special musculoskeletal model that takes into consideration the generated muscle forces of 27 musculotendon actuators and joint stiffness of the leg and allows the calculation of the motion trajectory of the robot and the forces that the robot needs to apply to the foot in each moment of the therapeutic exercise. Robotic treatment programs are customized for the individual patient by using a genetic algorithm (GA) that refers to the musculoskeletal model and calculates the parameters of the spline curves of the motion trajectory of the robot and forces acting on the foot.
The detection and evaluation of early faults is currently an important issue in the condition assessment of machinery and has been a challenging problem. In this paper, a novel index and a periodic enhancement group sparse (PEGS) model are sequentially proposed for early fault feature extraction of rolling bearings. Firstly, an index is defined based on the energy operator Gini index to determine the early fault occurrence from the whole-lifecycle vibration signal of the rolling bearing. Second, PEGS is proposed by appending a period estimating approach and a novel non-convex penalty to the non-convex group sparse optimization. It can promote the sparsity of fault signals and its ability to extract fault features. Moreover, the adaptive selection strategy for regularization parameter and group size is discussed. Simulations and two real experimental cases verify that the proposed method can determine the early fault occurrence point and extract the fault features more accurately than other comparison methods.
Many methods for physical medicine and rehabilitation strategies have been developed for physically disabled people. Most of them have been determined by physical therapists based on anatomical and physiological assessment of the subject. With application of robot technology which allows smooth achievement of complex trajectories, and with loading or assisting designated muscles in an accurate manner, a better rehabilitation strategy can be developed. In this paper, we propose a new method to design the motion trajectory of lower limbs and the applied forces acting on the foot.In order to improve the rehabilitation effect in full consideration of biomechanical stability of the trajectory and musculo-skeletal model of each subject, we have applied genetic algorithm to search the parameters of spline curves for the trajectory and the applied force acting on subject's foot. The usefulness of the proposed method is confirmed through simulation results and the comparison with a conventional training trajectory.
Through the analysis of principle and process of image signal denoising, a kind of image denoising algorithm based on Bidimensional Empirical Mode Decomposition is proposed. This paper has improved the traditional Bidimensional Empirical Mode Decomposition method. Bidimensional Empirical Mode Decomposition method is used to decompose the image signal and selective denoising is done to decomposition result by applying self-adaptive median filtering. Denoising result can fully retain the non-stationary feature which is inherent in image signal and it also has the characteristics of strong self-adaption, flexibility and effectiveness. Its computation speed and computational accuracy are greatly increased. It is proved by experiment that when processing noising image, this method not only greatly reduces the noise, but also retains the detail information like the edge of original image well. Keywords-bidimensional separable empirical mode decomposition; denoising; adaptive median filteringI.
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