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The classification of surface myoelectric signals (sEMG) remains a great challenge when focused on its implementation in an electromechanical hand prosthesis, due to its nonlinear and stochastic nature, as well as the great difference between models applied offline and online. In this work, the selection of the set of the features that allowed us to obtain the best results for the classification of this type of signals is presented. In order to compare the results obtained, the Nina PRO DB2 and DB3 databases were used, which contain information on 50 different movements of 40 healthy subjects and 11 amputated subjects, respectively. The sEMG of each subject was acquired through 12 channels in a bipolar configuration. To carry out the classification, a convolutional neural network (CNN) was used and a comparison of four sets of features extracted in the time domain was made, three of which have shown good performance in previous works and one more that was used for the first time to train this type of network. Set one is composed of six features in the time domain (TD1), Set two has 10 features also in the time domain (TD2) including the autoregression model (AR), the third set has two features in the time domain derived from spectral moments (TD-PSD1), and finally, a set of five features also has information on the power spectrum of the signal obtained in the time domain (TD-PSD2). The selected features in each set were organized in four different ways for the formation of the training images. The results obtained show that the set of features TD-PSD2 obtained the best performance for all cases. With the set of features and the formation of images proposed, an increase in the accuracies of the models of 8.16% and 8.56% was obtained for the DB2 and DB3 databases, respectively, compared to the current state of the art that has used these databases.

Sargassum has become an “ecological disaster” on most beaches in the Caribbean Sea, causing serious health, safety, economic and environmental problems. The objective of this study was to valorize sargassum for the extraction of sodium alginate, cellulose and cellulose nanocrystals. Cellulose was also used in the manufacture of a separator for an energy storage device (ESD) based on expanded graphite and activated carbon. Sargassum was characterized physicochemically; the results of this characterization are very relevant for the generation of bioenergy and removal of toxic contaminants. The extracted materials were identified by FT-IR; the spectra were very similar to the IR spectra of commercial samples, which corroborated that the materials extracted were sodium alginate, cellulose and cellulose nanocrystals. The performance of the ESD/cellulose-based separator was determined using cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS). Additionally, an RC circuit and an oscilloscope were used to perform a charge/discharge test. The voltammograms obtained for the ESD showed a rectangular shape with redox peaks characteristic of a pseudocapacitor and a battery, and the Nyquist plot showed a typical electrochemical performance of such ESD. Likewise, the developed devices have the capacity to store and release energy in seconds.