Machine Learning Techniques for Predicting Surface EMG Activities on Upper Limb Muscle

Abstract

The aim of this review study is to analyze the techniques for predicting the surface EMG activities on upper limb muscles using different machine learning algorithms. In this study, we followed a systematic searching procedure to select articles from four different online databases, i.e. PubMed, Science Direct, IEEE Xplore and Biomed Central (published years between 2010 and 2018). In our searching procedure, we searched by characteristically with two keywords (“EMG” and “Machine Learning”) in the above four listed databases to find the related articles in the field of machine learning techniques for predicting surface EMG activities on upper limb muscles. From the searching of this review, we selected total 25 articles for predicting surface EMG signals on upper limb muscles, where 10 articles are provided most efficient and effective classifier of surface EMG signals, 11 articles described different hand gesture recognition using machine learning algorithms, 2 articles explained that the importance of muscles selection, 1 article presented the natural pinching technique and 1 article focus on evaluation error rate of movements. This review presents not only the machine learning techniques for prediction of surface EMG activities on upper limb muscles but also it focuses on the challenge of the machine learning techniques for predicting surface EMG data. In addition, we believe that this review also provides muscle related issues that will impact the prediction of surface EMG activities on muscle.