Abstract
Widely accepted idea is the prosthetic control problem could be regarded as the pattern recognition problem. The prosthetic control means there are several differences such as distinguishable electric signals between different activation of muscle. However, this conventional method could not provide proper control of the artificial limbs. Kinematics behavior is continuous and needs the coordination of multiple physiological degrees of freedom (DOF) among various joints. Currently, a huge challenge is achieving precise, coherent and elegant coordination protheses which needs many DOFs to rehabilitation of patients with limb deficiency. This article analyzed the principles of control of bionic limbs from the aspect of EMG and traditional pattern recognition. According to the research results, the following conclusions can be given. Since the quantum amplitudes are complex numbers generally, different parameter should be included and analyzed together during the quantum information processing. Besides, the quantum control scheme could be combined with the classic one. What is more, other sensor modes should be applied for robust control instead of the EMG signal only.
Highlights
Amputees will apply the myoelectric protheses to regain the function of kinematics
Invasive Brain-Machine Interface (BMI) obtains the relationships between actual movements and neural signals which are recorded from cerebral cortex by signal processing technology, and provides users with communication and control channels to communicate with the outside world directly
Myocontrol signals are wildly used in assistive technology such as protheses and the coordination of multiple physiological degrees of freedom (DOF) across several joints
Summary
Amputees will apply the myoelectric protheses to regain the function of kinematics. The surface electromyographic(sEMG) control the muscular contractions which contains cerebral motile information. Invasive Brain-Machine Interface (BMI) obtains the relationships between actual movements and neural signals which are recorded from cerebral cortex by signal processing technology, and provides users with communication and control channels to communicate with the outside world directly. Myocontrol signals are wildly used in assistive technology such as protheses and the coordination of multiple physiological degrees of freedom (DOF) across several joints. There is no pattern classification-based methods currently could propose motor signals more than two different types categories of simultaneously. The surface electromyography (EMG) has been one of the most available methods interfacing between the patients and devices without tresis vulnus to attain the biofeedback [1]. The acquisition method of EMG signal is sorted out, and the factors that influence the surface. EMG signal, the existing problems and the contents of possible improvement methods are discussed
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