Abstract
The increasing amount of data in electromyographic (EMG) signal research has greatly increased the importance of developing advanced data analysis and machine learning techniques which are better able to handle “big data”. Consequently, more advanced applications of EMG pattern recognition have been developed. This paper begins with a brief introduction to the main factors that expand EMG data resources into the era of big data, followed by the recent progress of existing shared EMG data sets. Next, we provide a review of recent research and development in EMG pattern recognition methods that can be applied to big data analytics. These modern EMG signal analysis methods can be divided into two main categories: (1) methods based on feature engineering involving a promising big data exploration tool called topological data analysis; and (2) methods based on feature learning with a special emphasis on “deep learning”. Finally, directions for future research in EMG pattern recognition are outlined and discussed.
Highlights
Recognition of human movements using surface electromyographic (EMG) signals generated during muscular contractions, referred to as “EMG Pattern Recognition”, has been employed in a wide array of applications, including but not limited to, powered upper-limb prostheses [1], electric power wheelchairs [2], human-computer interactions [3], and diagnoses in clinical applications [4]
Kamavuako et al [27] found that there was no consensus on the optimum value of the threshold parameter of two of the most commonly used EMG features: zero crossings (ZC) and slope sign changes (SSC), leading them to investigate the effect of threshold selection on classification performance and on the ability to generalize across multiple data sets
The results showed that deep belief network (DBN) yields a better classification accuracy than linear discriminant analysis (LDA), support vector machine (SVM), and multi-layer perceptron neural network (MLP), but that the DBN requires lengthy iterations to attain good performance in recognizing EMG patterns without overfitting
Summary
Recognition of human movements using surface electromyographic (EMG) signals generated during muscular contractions, referred to as “EMG Pattern Recognition”, has been employed in a wide array of applications, including but not limited to, powered upper-limb prostheses [1], electric power wheelchairs [2], human-computer interactions [3], and diagnoses in clinical applications [4]. With the advent of shared bigger EMG data sets and recent advances in techniques for addressing overfitting problems, most emerging deep learning architectures and methods have been employed in EMG pattern recognition systems (e.g., [14,23,24]). In some cases, both feature engineering and learning are combined by inputing pre-processed data or pre-extracted features to a deep learning algorithm with some benefits having been shown (e.g., references [11,23,24]).
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