# Analysis of Surface EMG Parameters in the Overhead Deep Squat Performance
## 1 Introduction  
Electromyography (EMG) is a technique for detecting depolarization and repolarization that occur on the sarcolemma of the skeletal muscle fiber. It is used to diagnose muscle conditions and motor nerves that transmit electrical signals to initiate muscle contraction [1, 2, 3]. The analysis of electrical signals is commonly used in both post-traumaticand neurological rehabilitation and is becoming increasingly common in sports [4; 5; 6]. The EMG signal can be recorded by needle electrodes inserted through the skin into muscle tissue or electrodes placed on the skin above the measured muscle. In the latter case, the recorded signal is a superposition of individual motor unit action potential (MUAP) [7]. This method of EMG signal acquisition is referred to as surface EMG (sEMG). Surface EMG is often utilized in sports and physiotherapy [8] due to its non-invasive nature. In sports, for example, the standard Functional Movement Screen (FMS) exercises (one of them is the overhead deep squat) are a screening method used to assess the proper functioning and condition of the muscular system of an amateur trainer or athlete [6; 9]. In physical therapy, in turn, assessing the correctness of the exercise is crucial for the effectiveness of therapy and patient safety. The main goal of our work is to evaluate the parameters determined on the basis of sEMG, which could support objective analysis of muscle work during FSM exercises, especially in overhead deep squat. We are looking for the parameters that correlate well with how properly the deep squat is performed. In the future, the determined parameters could help in predicting and preventing injuries and also support movement optimization. Finding the appropriate parameters for monitoring fatigue and training effectiveness, allowing for personalization and improvement of athletes' training, is a difficult task; however, our attempt may contribute to supporting the efforts made in this field. Below, we present some literature reports on this topic. The correctness of physical exercises, for example, squats, is very often assessed by physiotherapists who evaluate parameters such as stance width, foot rotation, trunk position, tibia position, squat depth, and others [10]. There is also a great deal of the literature in which we can find studies on assessing the correctness of exercise performance based on the analysis of sEMG signals [11]. For example, in [12], step-up included the step-up, crossover step-up, diagonal step-up, and lateral step-up exercises, in which the activation of the chosen muscles was studied. The authors filtered sEMG signals with a band-pass filter of 10 to 450 Hz and used the magnitude of muscle excitation, which was represented by the root mean square (RMS) (125-millisecond moving window) of sEMG and expressed as a percentage of maximal voluntary isometric contraction (MVIC). The measure of the relative contributions of four hip and thigh muscles while performing squats at three depths was examined in [13]. In that paper, no details regarding sEMG signal processing were given. However, the signals were normalized by being expressed as a percent contribution to the total electrical activity of all the muscles examined. Muscle recruitment in the back squat was presented in [14]. To obtain the envelope of sEMG, the authors applied the bandpass filter at 10 to 450 Hz and smoothed the signal using the RMS algorithm with a 50 ms window time. In [15], the authors compared the muscle activation level during barbell deadlift and hex bar deadlift exercises. The sEMG signals were converted to root mean square signals (using a hardware circuit network (100 ms window)) and sampled at 100 Hz using a 16-bit A/D converter. The back squat and barbell were analyzed in [16]. The authors first filtered the sEMG using a 10-500 Hz bandpass filter, next used full-wave rectification, and then smoothed the data using RMS with a 100 ms window, and the peak data were normalized to a mean peak of a 1000 ms window from MVIC.