Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation <s> Introduction <s> Electromyography (EMG) signals can be used for clinical/biomedical applications, Evolvable Hardware Chip (EHW) development, and modern human computer interaction. EMG signals acquired from muscles require advanced methods for detection, decomposition, processing, and classification. The purpose of this paper is to illustrate the various methodologies and algorithms for EMG signal analysis to provide efficient and effective ways of understanding the signal and its nature. We further point up some of the hardware implementations using EMG focusing on applications related to prosthetic hand control, grasp recognition, and human computer interaction. A comparison study is also given to show performance of various EMG signal analysis methods. This paper provides researchers a good understanding of EMG signal and its analysis procedures. This knowledge will help them develop more powerful, flexible, and efficient applications. <s> BIB001 </s> Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation <s> Introduction <s> Fundamental changes over time of surface EMG signal characteristics are a challenge for myocontrol algorithms controlling prosthetic devices. These changes are generally caused by electrode shifts after donning and doffing, sweating, additional weight or varying arm positions, which results in a change of the signal distribution-a scenario often referred to as covariate shift. A substantial decrease in classification accuracy due to these factors hinders the possibility to directly translate EMG signals into accurate myoelectric control patterns outside laboratory conditions. To overcome this limitation, we propose the use of supervised adaptation methods. The approach is based on adapting a trained classifier using a small calibration set only, which incorporates the relevant aspects of the nonstationarities, but requires only less than 1 min of data recording. The method was tested first through an offline analysis on signals acquired across 5 days from seven able-bodied individuals and four amputees. Moreover, we also conducted a three day online experiment on eight able-bodied individuals and one amputee, assessing user performance and user-ratings of the controllability. Across different testing days, both offline and online performance improved significantly when shrinking the training model parameters by a given estimator towards the calibration set parameters. In the offline data analysis, the classification accuracy remained above 92% over five days with the proposed approach, whereas it decreased to 75% without adaptation. Similarly, in the online study, with the proposed approach the performance increased by 25% compared to a test without adaptation. These results indicate that the proposed methodology can contribute to improve robustness of myoelectric pattern recognition methods in daily life applications. <s> BIB002
The human upper limb is a significant part of the body, the partial or complete loss of which can have a significant effect on a person's ability on the day to day activities. The human upper limb has methods involve various approaches such as pre-processing, segmentation of data, feature extraction, feature classification and post-processing BIB002 . All these approaches related to myoelectric pattern recognition in one way or another can be helpful, but these methods still need further real-time evaluations for their validity  . Much research has been done on the myoelectric prosthesis; nevertheless, some of the areas in the field need to be improved: (i) control of multiple degrees of freedoms (DOFs) naturally and intuitively, (ii) two-way communication with the brain (peripheral nervous system (PNS)) and iii) fast learning. Moreover, several advanced pattern recognition techniques have been proposed without any real-world user applications BIB001  . A large portion of pattern recognition techniques described in the literature is still being applied in clinical settings. Moreover, the performance of these algorithms is affected greatly by several factors, including the positioning of electrodes, the fatigue of the muscle, arm position, surface EMG cross-talk and muscle contraction. This paper is mainly focused on a review of the major pattern recognition control approaches for myo-activated prostheses and their real-time amputee applications, and then suggests some critical directions to follow to improve their performance level while maintaining quantifiable viability. The research methodology is discussed in Section 2. Section 3 describes the pattern recognition techniques and systems used today, their merits and demerits if these are hands-on and what extents need to be innovative for these technologies to be accomplished. Section 4 describes the methods and analysis of the real-time application of myo-prosthesis. The results are discussed in Section 5. The issues and advances made concerning this research area are discussed in Section 6. Section 7 concludes and provides an overall analysis of the article.