## 4 Case Study: High-Frequency Tasks  
The main objective of this work is the development of a hardware-software system consisting of an electronic device for acquiring the sEMG signal and a processor integrated with the acquisition circuit used to store and send the data to a cloud platform. The received data will be analyzed and implemented with AI techniques for surface electromyography. Tools for signal acquisition and signal processing were used for its realization. In particular, biopotential electrodes, an acquisition circuit, a variable power supply device to power the circuit, and an acquisition board to convert the signal from analogue to digital were used for acquisition. In order to study and process the signal, an acquisition system was set up, i.e., an electronic measuring system whose purpose is to record and process the measurements of the emitted signal [43, 44]. The acquired signal is subjected to amplification to make it compatible with the specifications of the instrumentation. Filtering reduces the amount of noise that is added to the useful signal. After the conversion from analogue to digital form, the signal is sent to an integrated circuit consisting of a Raspberry processor, which has the task of taking the information acquired from the circuit and sending it to a cloud platform [45, 46], where the data will be processed and analyzed with AI algorithms. The hardware design of an EMG signal acquisition system involves careful consideration of various components and functionalities to ensure the effectiveness, safety, and reliability of the device. For this reason, targeted, constant, and intensive monitoring of EMG parameters was carried out. The monitoring was performed by a central unit consisting of an acquisition board and a processor for storing and processing signals from the electrodes interfaced to the acquisition circuit. The acquired signals are managed by an intelligent System On Module (SOM), i.e., an independent electronic device capable of managing a Linux operating system and carrying out complex numerical processing. The acquired signals are then sent to a database, via an Internet key, which stores them and makes them available to be handled and processed (Figure 1). The specific details may vary depending on the intended use and the technology involved. The acquisition circuit proposed in this study was designed according to the diagram shown in Figure 2.  
At the base of the instrumentation used is a variable power supply that was set at \(\pm 5\) volts and connected to the power terminals of the acquisition circuit. The circuit is equipped with three power supply terminals, three input terminals, i.e., the three pairs of electrodes that will be applied to the subject's skin, and three output cables that are used to connect the circuit with the acquisition board. As far as amplification is concerned, as  
Figure 1: Integrated electronic system flow chart. Figure 2: Schematic diagram of the acquisition circuit with front-end amplifier. detailed in Figure 3, the RG resistor allows for a gain of G = 1000 (60 dB) using the specific pin configuration of the AD623 amplifier. The passive notch filter makes it possible to eliminate the specific frequency of f = 50 Hz by means of a combination of various resistors and capacitors, which, thanks to their particular positioning, create a system consisting of a pole and two simple zeros that make it possible to stop the frequency that causes noise. Finally, an active third-order low-pass filter was added to attenuate all frequencies above the cutoff frequency f = 150 Hz, with a trend of \(-\)60 dB per decade. In addition, two capacitors, of capacitance C = 0.1 \(\mu\)F, were added between the ground and the amplifier supplies. This addition is necessary, as these capacitors have the function of noise suppressors, absorbing voltage peaks usually coming from the power supply itself. For this reason, they have been mounted as close as possible to the power supply pins of the ICs.