###### 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. Keywords:hand gesture characterization; fuzzy entropy; permutation entropy; feature selection; EMG signal processing; Myo armband +
Footnote †: journal: Applied Soft  
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]. However, relatively less complex algorithms, such as linear discriminant analysis and decision trees, have also been employed for gesture recognition driven by myoelectric signals, providing fully satisfying outcomes [5,9]. Further, naive Bayes, quadratic discriminant analysis, recurrent neural networks [5,10] and architectures based on extended associative memories [11] have been also tested. More recently, novel machine learning and deep learning methods have been investigated for hand motion classification [12, 13, 14]. However, irrespective of the particular method adopted for gesture recognition, a number of challenging topics remain still open in the field of gesture recognition, such as the type and location of electrodes [4,15] and the most appropriate signal processing for sEMG feature extraction and selection, which still represents a fundamental issue [2,3,7,9]. Regarding the former aspect, the most common procedure is choosing a pair of forearm muscles, e.g., the flexor and extensor carpi radialis [16,17], or adding to the former also upper-arm-placed probes [18], with a single-differential electrode configuration or by using multi-channels sEMG arrays [4], which allow one to detect more spatially located muscle characteristics, such as fiber length, conduction velocity or temporal characteristics of single motor units [1,3], by means of the sEMG signal decomposition technique [4]. However, in more recent years, the use of low-cost and off-the-shelf devices gained increasing traction for myoelectric-based hand gesture recognition and prosthesis control. In this field, the Myo armband (Thalmic, Ontario, Canada), despite some obvious technical limitations, e.g., a low sampling rate, represents one of the most popular devices for upper limb sEMG signal acquisition, being affordable, easy to set and use and unobtrusive [2,7,10,11,14,19]. Further, it was also used for publicly available datasets [20] and for myoelectric control of prosthetic hands [11]. On the other hand, in the field of gesture recognition, and in prosthetic control more generally, reliable feature selection is a key factor [2,3,9].