Structured Learning and Decomposition of Fuzzy Models for Robotic Control Applications
Journal of Intelligent and Robotic Systems
Mean frequency derived via Hilbert-Huang transform with application to fatigue EMG signal analysis
Computer Methods and Programs in Biomedicine
IEEE Transactions on Robotics - Special issue on rehabilitation robotics
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part I
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To improve the quality of life for the disabled and elderly, this paper develops an upper-limb, EMG-based robot control system to provide natural, intuitive manipulation for robot arm motions. Considering the non-stationary and nonlinear characteristics of the Electromyography (EMG) signals, especially when multi-DOF movements are involved, an empirical mode decomposition method is introduced to break down the EMG signals into a set of intrinsic mode functions, each of which represents different physical characteristics of muscular movement. We then integrate this new system with an initial point detection method previously proposed to establish the mapping between the EMG signals and corresponding robot arm movements in real-time. Meanwhile, as the selection of critical values in the initial point detection method is user-dependent, we employ the adaptive neuro-fuzzy inference system to find proper parameters that are better suited for individual users. Experiments are performed to demonstrate the effectiveness of the proposed upper-limb EMG-based robot control system.