## 3 Challenges in Electromyography  
### Physical Principles  
Electromyography (EMG) is based on the fundamental principles of neurophysiology and allows to study the nervous system by recording the electrical signal of a muscle that spreads during contraction [29]. Often, electromyographic examinations are combined with other diagnostic methods, such as electroneurography, computed axial tomography, and magnetic resonance imaging, and other methods of analysis and diagnosis are necessary to evaluate the presence of any pathologies in the patient [30; 31; 32]. The electromyographic signal depends on various factors such as anatomical, physiological, and muscle properties and the state of the peripheral nervous system, so it is a very complex signal. However, its usefulness goes beyond simple functional analysis, as it also serves as a diagnostic tool to outline the location and severity of neurological lesions. When the fibers of the skeletal muscle contract, they conduct an electrical activity (action potential, AP) that can be measured by electrodes placed on the skin surface above the muscles [33]. Small functional groups of muscle fibers, called motor units (MUs), contract synchronously, giving rise to a potential of action of the motor unit (MUAP). To support the force, an MU is activated repeatedly by the central nervous system several times per second. The repeat frequency, or average, is often between 5 and 30 times per second (or faster). The amplitude of the signal varies between 20 and 2000 \(\mu\)V. The electromyographic signal (EMG) is widely used as a suitable means to access the physiological processes involved in the production of joint movements. The EMG surface (sEMG) measures the electrical potential resulting from the overlap of the action potential of individual muscle fibers. The use of sEMG simplifies signal acquisition not only because of the non-invasiveness of the procedure, but also because the acquisition can be carried out while performing functional exercises. The information extracted from EMG signals can be exploited in various applications. The sensors typically used for EMG measurement are needle sensors (unipolar or bipolar). In the latter case, two sensors are placed on the muscle under analysis. The experimental data analyzed here come from non-invasive surface EMG sensors, which have the cross-talk effect, i.e., the detection of electrical activity by several muscles simultaneously in action. In addition, there are several sources of \"noise\" that influence the recording. The impedance of the electrodes and the slight variation in the electrode positions suggest the use of appropriate techniques for filtering the sEMG data. These cells exhibit distinct resting and action potentials, with potential differences across their membranes typically spanning \(-\)40 to \(-\)90 mV. As a discipline at the intersection of physiology and technology, EMG measurement encounters various challenges. To obtain optimal signals, efficient filtering techniques are required, and the type of EMG measurement must be accurate, such that it provides signals with less noise and greater reliability. The signal amplitude of the surface EMG measurements is analyzed to estimate the level of muscle contraction, while the frequency component is used to assess muscle activation performance. However, the amplitude of the detected EMG signal and the average frequency are influenced by the position of the surface electrodes, although the action potentials in a muscle are generated simultaneously. The fairly wide voltage range means that the signals are not reproducible. For this reason, the resting potential best represents an indication of the membrane potential under normal conditions. [PERSON]'s equation (Equation (1)) provides a framework for calculating the resting potential, incorporating key variables such as membrane concentration and resistance.