Abstract
The surface electromyography signal (sEMG) is widely used for gesture characterization; its reliability is strongly connected to the features extracted from sEMG recordings. This study aimed to investigate the use of two complexity measures, i.e., fuzzy entropy (FEn) and permutation entropy (PEn) for hand gesture characterization. Fourteen upper limb movements, sorted into three sets, were collected on ten subjects and the performances of FEn and PEn for gesture descriptions were analyzed for different computational parameters. FEn and PEn were able to properly cluster the expected numbers of gestures, but computational parameters were crucial for ensuring clusters’ separability and proper gesture characterization. FEn and PEn were also compared with other eighteen classical time and frequency domain features through the minimum redundancy maximum relevance algorithm and showed the best predictive importance scores in two gesture sets; they also had scores within the subset of the best five features in the remaining one. Further, the classification accuracies of four different feature sets presented remarkable increases when FEn and PEn are included as additional features. Outcomes support the use of FEn and PEn for hand gesture description when computational parameters are properly selected, and they could be useful in supporting the development of robotic arms and prostheses myoelectric control.
Highlights
Electromyography represents one of the most used techniques adopted to extract information about the control of movement carried out by the central nervous system [1]
Results of the present study suggest the suitability of fuzzy entropy (FEn) to describe a wide range of arm gestures, since the FEn was tested on three different sets of hand gestures: movements miming finger numbering, wrist movements and more complex gestures, involving both fingers and wrist, for a total of 14 gestures
The use of two complexity measures, the FEn and permutation entropy (PEn), appeared suitable for properly characterizing surface electromyography (sEMG) signals recorded from the upper limb for hand gesture recognition purposes
Summary
Electromyography represents one of the most used techniques adopted to extract information about the control of movement carried out by the central nervous system [1]. The sEMG signals are perhaps the most used bio-signals when dealing with lower and upper limb prosthesis control, and in this field a significant interest is recognizable in hand gesture recognition from forearm myoelectric activity [3,4,5]. From the seminal work by Lee and Saridis [6], the field of hand gesture recognition from sEMG signals gained a dramatic increase in terms of related research [7], which seems to be focused on two main. Sci. 2020, 10, 7144 subtopics, i.e., classification architectures and feature selection. Regarding the former, machine learning algorithms still result in the most used classifiers when dealing with sEMG-based gesture recognition, e.g., support vector machines, artificial neural networks and the k-nearest neighbors [2,8]
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