Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation <s> Segmentation of Data <s> This paper represents an ongoing investigation of dexterous and natural control of upper extremity prostheses using the myoelectric signal (MES). The scheme described within uses pattern recognition to process four channels of MES, with the task of discriminating multiple classes of limb movement. The method does not require segmentation of the MES data, allowing a continuous stream of class decisions to be delivered to a prosthetic device. It is shown in this paper that, by exploiting the processing power inherent in current computing systems, substantial gains in classifier accuracy and response time are possible. Other important characteristics for prosthetic control systems are met as well. Due to the fact that the classifier learns the muscle activation patterns for each desired class for each individual, a natural control actuation results. The continuous decision stream allows complex sequences of manipulation involving multiple joints to be performed without interruption. Finally, minimal storage capacity is required, which is an important factor in embedded control systems. <s> BIB001 </s> Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation <s> Segmentation of Data <s> This paper introduces and evaluates the use of Gaussian mixture models (GMMs) for multiple limb motion classification using continuous myoelectric signals. The focus of this work is to optimize the configuration of this classification scheme. To that end, a complete experimental evaluation of this system is conducted on a 12 subject database. The experiments examine the GMMs algorithmic issues including the model order selection and variance limiting, the segmentation of the data, and various feature sets including time-domain features and autoregressive features. The benefits of postprocessing the results using a majority vote rule are demonstrated. The performance of the GMM is compared to three commonly used classifiers: a linear discriminant analysis, a linear perceptron network, and a multilayer perceptron neural network. The GMM-based limb motion classification system demonstrates exceptional classification accuracy and results in a robust method of motion classification with low computational load. <s> BIB002 </s> Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation <s> Segmentation of Data <s> Pattern recognition-based control of myoelectric prostheses has shown great promise in research environments, but has not been optimized for use in a clinical setting. To explore the relationship between classification error, controller delay, and real-time controllability, 13 able-bodied subjects were trained to operate a virtual upper-limb prosthesis using pattern recognition of electromyogram (EMG) signals. Classification error and controller delay were varied by training different classifiers with a variety of analysis window lengths ranging from 50 to 550 ms and either two or four EMG input channels. Offline analysis showed that classification error decreased with longer window lengths (p < 0.01 ). Real-time controllability was evaluated with the target achievement control (TAC) test, which prompted users to maneuver the virtual prosthesis into various target postures. The results indicated that user performance improved with lower classification error (p < 0.01 ) and was reduced with longer controller delay (p < 0.01 ), as determined by the window length. Therefore, both of these effects should be considered when choosing a window length; it may be beneficial to increase the window length if this results in a reduced classification error, despite the corresponding increase in controller delay. For the system employed in this study, the optimal window length was found to be between 150 and 250 ms, which is within acceptable controller delays for conventional multistate amplitude controllers. <s> BIB003 </s> Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation <s> Segmentation of Data <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> BIB004
The results obtained from the Pre-processed EMG signal (random nature) is not regarded as a useful input in the pattern recognition technique. Thus, to extract the descriptive features, the window (segmentation) of the Pre-processed data is required. There are mainly two different types of windowing techniques proposed: overlapping window and non-overlapping (adjacent) window. In overlapped windowing technique, the former window overlaps over the current window with increment timeless than the window length itself BIB004 . The window length should be selected properly in real-time, which could deliver an acceptable delay. Larger window length would provide high classification accuracy but delay in the classifier's decision. The different optimal length used in various works of literature has been reported in Tables 1-3 . Segmentation of EMG data (using windowing) helps in estimating the intended motion for the myoelectric classifier. It helps in the decision making of intended motion while new data are being acquired. Englehart and Hudgins BIB001 used an adjacent, disjoint analysis window length equivalent to 0.25 × Sample frequency (SF) (256 ms for SF of 1000 Hz) for continuous myoelectric classification BIB002 . They also demonstrated that the data segment length of 0.125 × SF (128 ms for SF of 1000 Hz) or even less as 0.03125 × SF (32 ms) could be considered, without much reduction in accuracy for the continuous segmentation of steady-state signal. As with the advanced real-time computation facility and high-speed processors, data processing could take less than 5 ms, thereby classifying data segment length could vary from 32 to 25 ms. In this approach, with the time increment less than segment length, the new segment could slide over the current segment. The segment length must be higher than the processing period because the mainframe feature set had been calculated and must take a choice earlier to the next segment. Thus, normally, the denser yet semi-class decisions are made through small segment increments that help to improve response time and accuracy BIB003 .