Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation <s> Pattern Recognition-Based Myoelectric Control <s> Using electromyogram (EMG) signals to control upper-limb prostheses is an important clinical option, offering a person with amputation autonomy of control by contracting residual muscles. The dexterity with which one may control a prosthesis has progressed very little, especially when control- ling multiple degrees of freedom. Using pattern recognition to discriminate multiple degrees of freedom has shown great promise in the research literature, but it has yet to transition to a clinically viable option. This article describes the pertinent issues and best practices in EMG pattern recognition, identifies the major challenges in deploying robust control, and advocates research directions that may have an effect in the near future. <s> BIB001 </s> Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation <s> Pattern Recognition-Based Myoelectric Control <s> This study presented a closed-loop adaptive calibration (CLAC) scheme where subjects could get instantaneous feedback of their movements and alter their motions immediately to update the model parameters to enhance its ability. The real-time performance was compared between the conventional open-loop calibration (OLC) and the presented CLAC based on three metrics (motion-selection time, motion-completion time and motion-completion rate). The CLAC performed slightly better than the OLC, but the difference was not significant. This was the first study designed to investigate the effects of CLAC for pattern recognition-based myoelectric control (discrete movement). The CLAC could be potentially applied in the multiuser interface to make the adaptation of the common model to a novel user efficiently and flexibly. <s> BIB002
Myoelectric control systems can be classified as a pattern recognition control system and a non-pattern recognition control system. The non-pattern recognition control includes onset analysis, proportional level control and threshold level control. All of them are easy to implement in a real system but are limited in their degrees of freedom. From the early 1960s onwards, pattern recognition-classification techniques attracted the attention of the research community working on controlling artificial limbs. This approach consists of segmentation of data, feature extraction, and classification of a set of features or patterns for the various mode of myo-activations  BIB001 . Figure 1 shows some of the existing pattern recognition techniques used for myoelectric controls. Feature extraction and windowing are two different parts of the BIB002 segmentation of data. Several studies use a pre-processing stage before the feature extraction to avoid the preliminary level of inherent disturbances and electromagnetic interferences. The output of pattern recognition is categorised into different classes or labels based on the input feature extracted. The classes define the control of the actuator with a specific command. In the next (sub) sections, we explain the detailed steps involved in real-time pattern classification of EMG based prosthetic hands. For simplicity, we summarised all the results (processing steps) as (a) real-time collected data from amputee (Table 1) , (b) real-time using embedded packages ( Table 2 ) and (c) real-time with virtual reality ( use a pre-processing stage before the feature extraction to avoid the preliminary level of inherent disturbances and electromagnetic interferences. The output of pattern recognition is categorised into different classes or labels based on the input feature extracted. The classes define the control of the actuator with a specific command. In the next (sub) sections, we explain the detailed steps involved in real-time pattern classification of EMG based prosthetic hands. For simplicity, we summarised all the results (processing steps) as (a) real-time collected data from amputee (Table 1) , (b) real-time using embedded packages ( Table 2 ) and (c) real-time with virtual reality (Table 3) , respectively.