# Deep Learning Methods to Analyze the Forces and Torques in Joints Motion
## 1 Introduction  
Saber fencing, known for its quick-paced and dynamic nature, has a rich history, with its roots tracing back to the Mongol Empire from 1206 to 1368, long before the modern sport was formalized. After its development for centuries [1; 2], although physical contact is replaced by electric blades and lame, which send signals to the display when a fencer is hit, fencing is still considered a very competitive combat sport. This category in fencing demands extremely fast reflexes, as evidenced by the fastest recorded fencing response being about 13.5 ms [3]. As a result, competitive saber fencers are often trained to perform movements such as extended lunges, \"flunges\" (fly + lunge), and speed footwork, which impose a high demand on the ankles and knees of athletes [4]. With the development of computer vision and machine learning, data-oriented analysis algorithms offer a new dimension in understanding and enhancing fencing performance. The collection and analysis of large datasets from fencing matches--including motion capture data, sensor readings, and video footage--enable the identification of patterns and trends that are not readily apparent through traditional observation. Previous machine learning application in fencing falls mainly into two categories. First, machine learning is applied to interactions between fencers. Machine learning is trained as motion capture algorithms in fencing scoring [5] with an accuracy of 89.1%. The prediction algorithms for fencers in competitions are also reported to aid fencers in mastering games [6]. Additionally, sword tip tracking is also modeled with an algorithm that predicts the next movement of the tip based on the current tip position [7]. Second, algorithms for motion analysis are another critical concern about fencing. There are also video-based classification and correction models for fencing style analysis and improvement [8]. A neuralnetwork-based gait tracking algorithm is also designed to identify factors influencing peak horizontal speed and differences across skill levels [9]. However, the risk of injuries, especially to the ankles and joints of these highly competitive fencers, has not been extensively investigated. The common injuries in fencing, including sabers, often involve strained muscles and ligaments, as well as twisted knees and ankles [10]. Ankle sprains are another common injury in fencing, accounting for a significant portion of all fencing injuries. They happen due to the ballistic movements, quick stop-starts, direction changes, and strong lunging involved in the sport. The repeated high mechanical demands placed on the musculoskeletal structures during fencing contribute to these injuries [11; 12]. To understand the injury, forces and related concepts must be understood. Classical mechanics, with its foundational principles of motion and force, serves as a robust framework for modeling the dynamics of fencing. By applying [PERSON]'s laws of motion, we can develop equations that describe the kinematics and kinetics of a fencer's movements. However, before the introduction of modern vision algorithms, using sensors to capture data are the major way for fencing analysis [11; 13]. However, the introduction of modern image processing algorithms, classical mechanics, and image processing algorithms simplified the data collection for a comprehensive understanding of fencing dynamics. Such a dual approach not only aids in the development of more effective training regimens and injury prevention strategies, but also enhances the competitive edge of athletes by leveraging data-driven insights. As fencing continues to evolve, the synergy between classical mechanics and modern data science will play a crucial role in pushing the boundaries of athletic achievement, ensuring that the sport remains both scientifically grounded and dynamically innovative. So, in this work, a new machine learning model is proposed and applied in the perspective of force and torque analysis.