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.

This study presents an innovative methodology for the detection of faults in electric motors, specifically in single-phase induction motors, using the Hilbert Transform combined with spectral density analysis. The main innovation lies in the integration of Field-Programmable Gate Array (FPGA) technology with the Hilbert Transform for real-time data acquisition and accurate signal analysis, allowing early detection of faults such as short circuits in the coils. The system uses an ADC to capture current and voltage signals, and a FIFO memory buffer to ensure continuous acquisition without data loss. The Hilbert Transform allows the decomposition of the signals and extraction of analytical frequencies, which facilitates the identification of fault-generated harmonics. The results show a significant improvement in fault detection, with the identification of high-frequency harmonics indicating internal problems such as short circuits. This approach, by integrating FPGA for fast signal acquisition and processing, optimizes the monitoring of electric motors, enabling more effective predictive maintenance and reducing downtime in industrial applications. The innovation of this system improves the accuracy and efficiency of fault diagnosis, contributing to the advancement of real-time monitoring technology.

Some industrial and laboratory applications such as control, monitoring, test and measurements, and automation require real-time systems for their development. Embedded systems for acquisition and processing often require the participation of the embedded operating system and therefore are necessary techniques that can accelerate software execution. The latest field-programmable gate arrays’ (FPGA) technology has blurred the distinction between hardware and software with embedded processors that allow the development of Systems-on-a-Chip (SoC) running on operating systems. The widespread adoption of wireless technologies such as Bluetooth, ZigBee, and Wi-Fi in the last years has facilitated the use of these technologies to the development of real-time monitoring applications that combined with FPGA devices which has the advantages of low cost, flexibility, and scalability as compared with other commercial systems.

This paper presents the development of a three-phase acquisition system designed to capture voltage and current signals from an electric motor. The platform supports a sampling rate of up to 82.5kHz across six channels, handling input levels of up to 440 V_ac and 100 A. The implementation integrates hardware architectures in VHDL for acquisition control, data storage, video generation, and peripheral configuration, supported by embedded system development tools. A customized version of Linux, adapted for the SoC-FPGA platform, serves as the operating system. In addition, a graphical interface was developed to enable parameter reconfiguration and real-time visualization of the acquired signals. Computational tasks such as the Fourier transform are executed directly in hardware to accelerate the extraction of the motor’s current signature. Furthermore, the Welch method is applied to estimate the power spectral density of the three-phase motor, with the system capable of periodically storing the obtained results.