ABSTRACT
In this study, based on kinematics data in steppage gait, a finite element model of human gait with ankle-foot orthosis (AFO) has been proposed to optimize the role of AFO through minimizing stress in the patients' sole. The required kinetics data for the model were captured through a force plate and then analysed by 3D-DOCTOR and ANSYS software. In the proposed three-dimensional finite element model the transmitted tension in soft tissue and bones during gait was calculated. By changing the thickness and materials of different layers of sole in AFO the tension variations have been assessed. Unlike previous studies, the effect of orthosis on tension generated in bones and muscles has been dynamically and continuously modelled and the contact between AFO and sole has been considered in this model. By using the optimized sole the stress distribution has been changed by +50.38% in the forefoot, +15% in the midfoot and -37.79% in the hindfoot. The tension reduction in the sole can improve the effect of AFO during abnormal gait. It is possible to design each orthosis sole based on the kinetics data of each patient.
Subject(s)
Ankle Joint/physiopathology , Foot/physiopathology , Gait Disorders, Neurologic/therapy , Orthotic Devices , HumansABSTRACT
INTRODUCTION: This paper deals with the dynamic modelling of human walking. The main focus of this research was to optimise the function of the orthosis in patients with neuropathic feet, based on the kinematics data from different categories of neuropathic patients. METHODS: The patient's body on the sagittal plane was modelled for calculating the torques generated in joints. The kinematics data required for mathematical modelling of the patients were obtained from the films of patients captured by high speed camera, and then the films were analysed through a motion analysis software. An inverse dynamic model was used for estimating the spring coefficient. RESULTS: In our dynamic model, the role of muscles was substituted by adding a spring-damper between the shank and ankle that could compensate for their weakness by designing ankle-foot orthoses based on the kinematics data obtained from the patients. The torque generated in the ankle was varied by changing the spring constant. Therefore, it was possible to decrease the torque generated in muscles which could lead to the design of more comfortable and efficient orthoses. CONCLUSION: In this research, unlike previous research activities, instead of studying the abnormal gait or modelling the ankle-foot orthosis separately, the function of the ankle-foot orthosis on the abnormal gait has been quantitatively improved through a correction of the torque.
Subject(s)
Biomechanical Phenomena , Gait Disorders, Neurologic/physiopathology , Gait Disorders, Neurologic/rehabilitation , Guillain-Barre Syndrome/physiopathology , Guillain-Barre Syndrome/rehabilitation , Image Processing, Computer-Assisted , Models, Anatomic , Models, Theoretical , Splints , Walking/physiology , Adolescent , Ankle Joint/physiopathology , Computer Graphics , Humans , Male , Muscle, Skeletal/physiopathology , Neural Networks, Computer , Prosthesis Design , Software , TorqueABSTRACT
This paper presents a new fuzzy-logic-control based filter with the ability to remove impulsive noise and smooth Gaussian noise, while, simultaneously, preserving edges and image details efficiently. To achieve these three image enhancement goals, we first develop filters that have excellent edge-preserving capability but do not perform well in smoothing Gaussian noise. Next, we modify the filters so that they perform all three image enhancement tasks. These filters are based on the idea that individual pixels should not be uniformly fired by each of the fuzzy rules. To demonstrate the capability of our filtering approach, it was tested on several different image enhancement problems. These experimental results demonstrate the speed, filtering quality, and image sharpening ability of the new filter.
ABSTRACT
The Marquardt algorithm for nonlinear least squares is presented and is incorporated into the backpropagation algorithm for training feedforward neural networks. The algorithm is tested on several function approximation problems, and is compared with a conjugate gradient algorithm and a variable learning rate algorithm. It is found that the Marquardt algorithm is much more efficient than either of the other techniques when the network contains no more than a few hundred weights.
