Myoelectric prostheses are a viable solution for people with amputations. The challenge in implementing a usable myoelectric prosthesis lies in accurately recognizing different hand gestures. The current myoelectric devices usually implement very few hand gestures. In order to approximate a real hand functionality, a myoelectric prosthesis should implement a large number of hand and finger gestures. However, increasing number of gestures can lead to a decrease in recognition accuracy. In this work a Myo arm band device is used to recognize fourteen gestures (five build in gestures of Myo armband in addition to nine new gestures). The data in this research is collected from three body-able subjects for a period of 7 seconds per gesture. The proposed method uses a pattern recognition technique based on Multi-Layer Perceptron Neural Network (MLPNN). The results show an average accuracy of 90.5% in recognizing the proposed fourteen gestures.
<div>This paper reviews the position/force control approach for governs an efficient knee joint in an active lower limb prosthesis, and the inter facing current control algorithm with human gate parameter is inserted. Two techniques are used to collect gait cycle data of leg: first, the foot ground force is obtained by the force platform device based on its position (x, y), then data of knee joint angles is recorded by using a video-camera device.The collected information is sent and used in the proposed intelligent controller. This intelligent control system used an adaptive neuro-fuzzy inference system (ANFIS) circuit in addition to the proportional integral derivative (PID) controller. This hybrid ANFIS-PID control system simulates and provides the ground force values. The experimental results show anexcellent response and lower root mean square error (RMSE) compared with each of PID and ANFIS controller that implemented for a similar purpose. In summary, the results showed acceptably stable performance of the proposedposition/force controller based on hybrid ANFIS-PID system. It can be concluded that the finest performance of the controlled force, as quantified by the RMSE criteria, is perceived by the proposed hybrid scheme depending on the controller intelligent decision circuit.</div>
The number of Above Knee (AK) amputees has increased in recent years and this has led to a need for urgent work on the design of proper lower limb prostheses. Lower limb prosthetics can be divided into active and passive devices. However, passive prosthetics cannot fully provide the natural motion of a healthy leg, and the technologies used in active prosthetics with knee joints are often far too expensive for amputees in developing countries such as Iraq. In this paper, an active lower limb prosthesis with an efficient knee joint is thus designed. Two strategies were used to collect data for gait cycle analysis of the leg in the sagittal plane: the first was based on the use of a force platform device to obtain the foot ground force according to the foot position (x, y), while the second utilised a video-camera based system to examine knee joint angles. The obtained data were all sent to an intelligent controller that uses an Adaptive Neuro-based Fuzzy Inference System (ANFIS). The ANFIS controller determines the ground force, mimicking the moment of the active knee with a DC motor and flexion-extension angle values. The experimental data for the motion of the knee joint were collected in the Gait Laboratory, then transformed to joint angles using the ANFIS controller. The results show excellent response in the proposed ANFIS controllers in terms of determining angle and moment values of the knee joint with a very low RMS error of 0.006.
This paper proposes the Adaptive Neuro-Fuzzy Interference System (ANFIS) method to realize the track correlation of Radar. ANFIS is used for the first time in inverse model in addition to model of aircraft position radar from the recorded data. The simulation results show that the proposed ANFIS controller has been successfully implemented. Root mean square error is applied to measure the performance of ANFIS that revealed the optimal setting needed for better estimation of the aircraft position. Results with RMSE less than 10-4 also show that the controller with ANFIS yields good tracking performance, valuable and easy to implement.
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