## 2 Related Works  
Electromyography (EMG) has been used as a measure to track the progress of rehabilitation and to identify diseases that affect muscle activation patterns. Although widely used, conventional EMG recording and interpretation techniques lack the ability to provide accurate signal detection and robust classification accuracy. In recent years, thanks to advances in materials science and artificial intelligence, EMG detection techniques have improved at a rapid pace. Materials that allow for greater biocompatibility have improved the quality of the data recorded by the electrodes. The use of machine learning algorithms has opened up new ways to understand complex EMG signals, enabling different, new, and improved application scenarios within the healthcare framework. To interpret the EMG signal, some studies have used algorithms based on machine learning or deep learning techniques for the pre-processing and interpretation of EMG signals. Raw EMG data are collected from subjects using surface or needle electrodes [20], and a high-pass filter with a cutting frequency of 2 Hz [21] is used to remove DC offset and low-frequency noise. To eliminate the interference of the feed frequency, a notch filter at 50 Hz is adopted, while the implementation of the signal can be carried out with classification algorithms for better results. Some studies present a non-invasive method of superficial EMG to support the diagnosis of Amiotropic Lateral Sclerosis (ALS). Using a flexible surface electrode applied to the thoracic and primary dorsal intersonic (FDI) muscles, while the reference electrodes were located near the elbow, EMG signals were processed with a linear discriminating analysis (LDA) classifier, obtaining a diagnostic performance of 90% sensitivity and 100% specificity for the diagnosis of ALS subjects [22]. Other studies, however, evaluated a biomarker that could predict the disease of patients with chronic obstructive lung disease (COPD). The development potential during their hospitalization period was acquired by the neural respiratory pulse extracted from sEMG signals, by placing surface electrodes (Blue Sensor Q, Ambu, St Ives, UK) in the second intercoastal space, immediately adjacent to the sternum. Using the \(t\)-test or Wilcoxon-signed-rank tests, data were obtained with relatively high reception accuracy in 14 and 28 days [23]. Other work proposed a method to diagnose gastrointestinal diseases through patients' sEMG signals, adopting the BIOPAC Systems Model MP36 Data Acquisition Unit and placing the single-use gel surface electrodes on the area near the navel. The recording signals were pre-processed from the decomposition of the empirical model and then transmitted to a number of machine learning classifiers, including a quadratic SVM, a fine KNN, RUS Boosted trees, and a cubic SVM, among which the highest achieved an accuracy of 100% in the differentiation of diarrhea, constipation, and normal gastrointestinal situations [24]. Similarly, scientists developed a generic algorithm based on sEMG signals to automatically detect tonic-clonic seizures. Auto-regression (AR) and DWT were applied to extract characteristics, and an ANN was used to classify. This approach achieved 100% accuracy in the differentiation of people with bruxism from others [25, 26]. To help accurately diagnose unilateral spastic cerebral palsy based on EMG signals, a semi-automatic method was sought. Specifically, it was chosen to use auto-adhesive pairs of Ag/AgCl EMG bipolar surface electrodes (recording diameter 10 mm), which were placed on the muscles of the pronator teres (FT) and the pronator quadratus (PQ). The recorded signals were pre-processed and then presented to three EMG experts for intra-rater reliability analysis. [PERSON]'s alpha reliability estimate with 95% confidence intervals was used to validate the results, showing predictive accuracy of 96% positive and 91% negative [27].