## 1 Introduction  
Fencing, one of the oldest sports, places many demands on the body regarding neuromuscular coordination, strength, power, and musculoskeletal system impact. Fencers align the leading foot with the opponent's stance in the basic _en guard_ gesture and place the back foot at 90\({}^{\circ}\) to the leading foot to maintain stability [1]. Furthermore, a fencer's effectiveness is highly dependent on the quickness of his/her motions in response to the opponent's action and his/her ability to anticipate the opponent using visual, kinaesthetic (dodge and parry), and acoustic stimuli [2]. The lunge movement starts with upper body movement, combined with the lower legs through the kinetic chain at the ankle, knee, and hip joints, to move the sword as quickly as possible towards the opponent to achieve a touch [3]. All of the rapid \"propulsion\" and \"dodge\" movements of attack/defense further expose fencers to impacts, explosive forces, power absorption, and shear forces of various magnitudes, distributed asymmetrically across the body [1]. Resulting of these dynamic and repetitive movements in fencing matches, fencing injuries are quite prevalent among athletes. The use of technology in sports is rapidly increasing, and biomechanical analysis in most sports is routine at the elite level these days. Sports biomechanics allows detailed analysis of sports movements to improve sports performance and/or lessen injury risk. One of the significant aims of sports data analysis is to assist in training. Motion capture is one of the most frequently used techniques in sports analysis; however, recent technological developments have produced inexpensive, non-invasive, wearable sensors that are ideal for obtaining sports performance measures during training or competition. The usefulness of wearable sensors for sports analysis has been shown in several papers [4; 5]: they are a reliable tool, able to benefit athletes of all levels, especially when complemented within a sensor fusion network and provide a large amount of high accuracy biomechanical data. However, with the large amount of data produced, finding a method to identify a meaningful relationship between data and extract the relevant information became necessary. In healthcare, user monitoring can be carried out in different ways by ensuring different levels of awareness [6; 7; 8] and security [9; 10; 11] for diagnostic and/or therapeutic purposes [12; 13; 14; 15; 16; 17; 18; 19; 20]. In the same way, predictive modeling methodologies could be applied to prove the efficiency and effectiveness of various fencing assault strategies. It is essential to understand which data should be saved and how to use it best. Sports establishments have an advantage over other teams by finding an acceptable way to extract the meaning from the available data and turn it into practical knowledge [21]. Combining wearable sensors and ML algorithms could enhance training by providing tools for analyzing actions. Recent studies have shown that elite fencers exhibited sequential coordination of upper and lower limb movements with coherent muscle activation patterns, compared to novice fencers [1]. Thus, elite fencers can fine-tune the techniques of fencers qualitatively, and trainers can use them to identify areas where trainees can improve. The present study aimed to combine biomechanical data related to the lunge gesture and different machine learning approaches to find the best model to classify the levels of performance of fencers, elite or novice. This gave us a useful tool to improve athletes' performance and trainers' activity. The paper is organized as follows. Section 2 provides an overview of related work and technologies investigated as background knowledge, including fundamental notions of fencing and lunge movement determinants. Section 3 describes the collected biomechanical data and the Machine Learning methods that were trained to predict the fencers' categories (elite or novice).