## 4 Case Study: High-Frequency Tasks
The manufacturing of the prototype is reported in Figure 4.  
The EMG signal detected and acquired by the circuit is transmitted with a General-Purpose Input/Output (GPIO) connection to an 8-channel analogue-to-digital converter, specifically the MCP3008 converter. The ADC converter, configured in Python, is connection to a Raspberry processor, which has the task of storing and sending the data on a cloud platform, where the digitized signals are extrapolated from the processor's base data and then processed. This step is necessary because the Raspberry needs an external ADC converter, which is why we have used the GPIO, through which we can connect external electronic components and, among them, of course, an ADC converter. Specifically, the system processes three different stages: the first is to acquire the signal, the second is to display it, and the last is to save and send it. The electronic system consists of an acquisition process necessary to acquire the signal; a process of displaying the EMG signal during the process of Maximum Voluntary Concentration (MCV), which retrieves  
Figure 4: (**a**) Front panel of the Biopotential Signal Acquisition (EMG) circuit. (**b**) Internal view. Figure 3: Detail of the acquisition circuit in the schematic composition: (**a**) amplification; (**b**) notch; (**c**) low-pass filter. the file with the previously acquired data and performs some processing on the signal; and finally a \"Task\" display process, which consists of a program that can extract from the signals the various parameters in the time domain, such as the maximum peak, the average value, and the effective value (Root Mean Square). In the first phase, the signals are detrended and then subjected to a series of lower cut filtrations at 30 Hz and a higher cut frequency of 450 Hz. This allows the transition only to certain frequencies, including those in the range of 30 Hz to 450 Hz. After the filtering operation, the signal correction is applied, which makes all the values of the signal positive so that even the negative signals are twisted relative to the time axis. This operation allows for better signal reading and allows for the calculation of standard parameters such as the average, effective value (RMS), and maximum value. Subsequently, a fourth-order low-pass filter is set to a cut frequency of 5 Hz and allows the passing of frequencies below the value of the cut rate. The main purpose of the filtering is to eliminate the noise, and in this case, through the low-pass filter, an operation of the envelope that allows the removal of the interference signal. The ultimate purpose is to extract the individual processed signal (MCV). This value allows us to redefine the signal that will no longer be evaluated by a voltage but by a percentage; this operation takes the name of normalization and consists of acquiring the extent to which a subject can contract a muscle and then assigning the value of maximum contraction to the maximum value (corresponding to 1), and the contraction zero (corresponding a 0). Usually, for this measurement, the subject has a maximum contraction for a few seconds, and this process is repeated several times with a period of pause between one contraction and the other. This is a powerful method for comparing electromyographic data obtained from tests on different subjects. Normalizing sEMG signals compared to MVC is also a commonly used method to reduce variability between different recordings. For the implementation of the latest analysis of \"Task,\" the same blocks were used in the MCV. Therefore, there exist a process for reading the previously acquired signal, a process to perform the detrending of the signals, a filter that passes the band of the fourth order with a bandwidth ranging from 30 Hz to 450 Hz, a procedure for performing the correction of the signal, and a low-pass filter of the fourth order with a cutting frequency equal to 5 Hz.