## 1 Problem Introduction
This summation is represented by bipolar signals with a symmetrical distribution of positive and negative amplitudes, known as the interference pattern. The two most important parameters that influence the amplitude and density of the analyzed signals are the recruitment of MUs and their frequency of activation. These parameters are the main control strategies used by the central nervous system to regulate the contraction process and thus modulate the strength of the muscle involved. Biological tissues perform a low-pass filtering effect on the original signal, which is why the surface electromyography (sEMG) signal does not reflect the original amplitude and frequency but is only a representation of it [12,13]. The interference pattern of the sEMG signal is random in nature as the set of activated MUs constantly changes, and the effects of motor unit action potentials (MUAPs) arbitrarily overlap [14]. For the same motor task performed with the same force, it is unlikely to observe identical patterns in the signal because the number of recruited MUs and the frequency of activation change continuously. Applying filtering algorithms or selecting appropriate amplitude parameters attempts to limit the non-replicable part of the signal. This study proposes an integrated system for the acquisition, processing, and analysis of the sEMG signal using artificial intelligence (AI) techniques and supervised learning. Data were acquired using a device developed for sEMG. The study then processed the data using a convolutional neural network (CNN), a supervised learning technique inspired by biological processes, which has several applications in image and video recognition, as well as in bioinformatics. CNNs have proven to be highly effective in analyzing electrophysiological signals due to their ability to automatically extract and learn relevant features from raw data [15; 16; 17]. Applied to sEMG data, CNNs can identify numerous spatially independent patterns, each associated with a single time flow, providing a way to separate the different electrical signals from various muscle activities [18]. Unlike the variable nature of sEMG signals recorded from a single muscle in isolation, the CNN analysis of sEMG signals from several muscles simultaneously allows for the detection of highly reproducible components [19]. The use of filtering algorithms and the adjustment of amplitude parameters during signal processing and acquisition serve to mitigate the non-replicable aspects of the signal. The primary purpose of this study is to employ appropriate processing techniques to obtain a trace directly associated with a characteristic of the muscle, typically the generated force. The researchers in this paper demonstrate important applications in monitoring the work activities of different subjects, significantly improving the quality and safety of work. The developed electronic device consists of a signal acquisition circuit from six electrodes and a Raspberry processor. The integrated system aims to filter and store signals from the electrodes and send them to a cloud platform for further processing using an AI technique. The proposed model has the goal of analyzing sEMG data with a supervised learning approach. In particular, a one-dimensional convolutional neural network (1D-CNN) was designed to detect the presence of neuropathies in subjects by identifying distinctive patterns in the sEMG signals. The present document is organized as follows: Section 2 lists similar works in which there are different points of view from the one presented in the paper. Section 3 discusses the challenges of electromyography, particularly the physical characteristics and nature of the sEMG signal. Section 3 describes the electronic device, the sensors used, and the implemented acquisition system. The computational model is presented in Section 4. Section 5 reports the results obtained from the processing of experimental data. Finally, conclusions are drawn.