The conceptual design and proposed control methodology for a master-slave system that consists of an upper limb exoskeleton that acquires motion data to predict the motion of the user to control a robotic arm that mimics the motion of the user is presented. The exoskeleton master-slave unit in its conceptual design is also shown, with proposed electromyography (EMG) signals from sixteen muscles that are related to the seven basic motions of the human arm and accelerometers attached on the exoskeleton to predict motion of the user. The proposed control methodology for the master-slave system consists of four stages: data acquisition, data processing, data analysis and motion controls. This concept is developed to support motion prediction to aid rehabilitation and power assist.
The simulation of a miniature conceptual seven degree-of-freedom (DOF) robotic slave arm that is able to imitate the seven basic motions of a human arm is performed. The studies on the operating envelope are presented in this paper. The miniature robotic slave arm, a part of the master-slave arm system, is modeled in Solidworks to study the motion of the arm during operation. Simulation is done in Solidworks to justify that the robotic arm designed is able to perform the seven basic motions of a human arm to the intended range of motion. Simulation is also performed for different combination of joint motions to study the maximum reachable distance, the safe operation envelope, of the robotic arm for the X, Y and Z-axis.
An optimized approach aiming to improve classification accuracy of wrist movements via electromyography (EMG) signals is presented here. EMG signals of the different types of wrist movements are obtained from the NINAPRO database. Useful features are extracted from the EMG signals via the waveform length method. The developed optimized classification system contains two main modules, known here as (i) optimized neural network module and (ii) movement prediction module. The optimized neural network module is made up of multiple 2-class neural networks. During Stage 1 Classification, a group of neural network (named NNG_S1) is formed after analyzing the sensitivity computed from the training outcomes of each neural network. A new group of neural network (named NNG_S2) is later formed in Stage 2 Classification after initial elimination via Stage 1 Classification. Further analysis is performed via the movement prediction module to predict the final outcome of the classification. The overall average classification accuracy achieved via the optimized classification system is 8.3% higher than the conventional neural network. The results validate that the optimized classification system performs better than the conventional neural network, providing more accurate signals for manipulating of exoskeleton for rehabilitation purposes.
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