Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation <s> • <s> We present updated progress on the design, construction and testing of an upper-extremity prosthesis control system based on implantable myoelectric sensors. The miniature injectable implant consists of a single silicon chip packaged with transmit and receive coils. Preparation for human implantation of the IMES system is underway. As part of this process, critical design improvements in the IMES implant were required. Here we report improved functionality of the IMES implant, hardened protection against electrical malfunction and tissue damage. <s> BIB001 </s> Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation <s> • <s> In the past years, especially with the advent of multi-fingered hand prostheses, the rehabilitation robotics community has tried to improve the use of human-machine interfaces to reliably control mechanical artifacts with many degrees of freedom. Ideally, the control schema should be intuitive and reliable, and the calibration (training) short and flexible. This work focuses on medical ultrasound imaging as such an interface. Medical ultrasound imaging is rich in information, fast, widespread, relatively cheap and provides high temporal/spatial resolution; moreover, it is harmless. We already showed that a linear relationship exists between ultrasound image features of the human forearm and the hand kinematic configuration; here we demonstrate that such a relationship also exists between similar features and fingertip forces. An experiment with 10 participants shows that a very fast data collection, namely of zero and maximum forces only and using no force sensors, suffices to train a system that predicts intermediate force values spanning a range of about 20 N per finger with average errors in the range 10-15%. This training approach, in which the ground truth is limited to an "on-off" visual stimulus, constitutes a realistic scenario and we claim that it could be equally used by intact subjects and amputees. The linearity of the relationship between images and forces is furthermore exploited to build an incremental learning system that works online and can be retrained on demand by the human subject. We expect this system to be able in principle to reconstruct an amputee's imaginary limb, and act as a sensible improvement of, e.g., mirror therapy, in the treatment of phantom-limb pain. <s> BIB002 </s> Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation <s> • <s> Upper-limb amputation imposes significant burden on amputees thereby restricting them from fully exploring their environments during activities of daily living. The use of intelligent learning algorithm for electromyogram-pattern recognition (EMG-PR)-based control in upper-limb prostheses is considered as an important clinical option. Though the existing EMG-PR prostheses could discriminate multiple degrees of freedom (DOF) limb movements, their transition to clinically viable option is still being challenged by some confounding factors. Toward realizing a clinically viable multiple DOF prostheses, this paper first explored the principles and dynamics of the existing intelligently driven EMG-PR-based prostheses control scheme. Then, investigations on core issues including variation in muscle contraction force, electrode shift, and subject mobility affecting the existing EMG-PR prosthetic control scheme were reported. For instance, variation in muscle contraction force and subject mobility led to degradation in the performance of the EMG-PR controlled prostheses with approximately 17.00% and 8.98% error values, respectively, which are still challenging issues among others. Thus, this paper reports core issues and best practices with respect to intelligent EMG-PR controlled prosthesis, the major challenges in implementing adaptively robust control scheme and provides future research directions that may result in the clinical realization of intuitively dexterous multiple DOF EMG-PR-based prostheses in the near future. <s> BIB003
The prosthetic control unit should be increased, and appropriate pattern-recognition should be used for proper handling of the prosthetic device. The prosthetic device should be developed using low-cost materials, affordable to all amputees. • Intuitiveness can be developed by extracting the signals using ultrasound imaging BIB002 , force myography (FMG), TMR and Implantable myoelectric sensor BIB001 . One possible way to minimise or eliminate this drawback of EMG interferences is to develop an electromagnetic shielding technique BIB003 and implement the best filtering strategy. Rather than depending on existing proposed training, an intelligent adaptive prosthetic system should be developed and implemented. An intelligent EMG-PR system requires to represent a data stream accurately in real-time. It shows a possible way to restrict the deficiency in the prosthesis market. With such developments, users' expectations can be met, and thus, device adoption for everyday use can be increased. Feature extraction is a core of conventional EMG-based pattern recognition control. To achieve the real-time usability of prosthetic, issues related to the feature extraction should be addressed. Deep learning (machine learning method based on ANN) may be one possible way to solve the problem of feature extraction BIB003 . Thus, more research on deep learning in pattern recognition-based prosthesis control should be conducted.