## 2 Background and Related Works
### Related Works  
The usage of Machine Learning and its practical applications are of interest to sports scientists because they have significant consequences for their industry. In fact, it has already influenced:  
* The devices used to collect data;
* The knowledge obtained from device-gathered data (3D kinematic and vertical ground reaction forces may be predicted);
* The processing of data from devices (classification methods can separate data into relevant packages that would have previously required sports scientists to spend much time on them);* How processed data can improve our comprehension of athletic performance and injury risk prediction. The objectivity of decision-making in sports science is projected to improve significantly by using Machine Learning during the coming ten years. According to studies, Machine Learning can predict future injuries based on pre-season measurements, identify movement strategies within a cohort, allow the identification of movement-specific injury risk factors, and recognize healthy people who exhibit movement patterns that are similar to those of injured people [34]. [PERSON] and colleagues [35] trained a Support Vector Machine (SVM) to test whether it would be preferable to use an inertial measurement unit (IMU) or a Kinect to record and classify fencing footwork to aid in fencer and coach training. The detection with the Kinect proved to be highly effective; it simultaneously achieved 100% recall and 100% precision. The inertial-based technique had the best detection performance, with 99.38% recall and 98.77% precision. The proposed approach has proven to be effective at providing pertinent feedback. However, it would be advantageous to further increase accuracy, particularly for the hand-time parameter, to enable even the most experienced fencers to improve this movement. Kinect's algorithm was also built for frontal postures, whereas fencing footwork is seen from the side. As a result, more precise tracking might be accomplished with a method designed specifically for fencing activities with a greater sample rate. It is important to note that the sensor's proper body location must be identified for IMU to compete with Kinect [35]. In this regard, [PERSON] and colleagues [36] used five IMUs placed on the lumbar spine, thighs, and shanks to categorize proper and improper lunge techniques and precise lunge technique variations. The IMU data's time and frequency domain features were used to train and evaluate SVM, k-nearest neighbors, Naive Bayes, and random forest. Only the random forest was selected due to its superior classification performance and its classification performance effectiveness. They used the permutation feature importance method of the random forest to analyze the most crucial attributes. One IMU on the right thigh can have 78% sensitivity and 83% specificity with a full feature dataset. The left shank had the lowest sensitivity and specificity for binary classification when employing a single IMU, at 40% and 82%, respectively. Regarding multi-class classification and binary classification (which has an accuracy of 90%), the five-IMU setup performed best (70% accuracy). A smaller IMU set with three IMUs placed on the lumbar region and both shanks also yielded accurate classification results. Categorizing quality is comparable to utilizing all features, even just 20% of the top-ranked characteristics [36]. [PERSON] and colleagues [37] classified fundamental footwork movements (step forward, step backward, rapid lunge, lunge with increased speed, lunge with waiting, and jumping-sliding lunge) in a cohort of 10 fencers with varying levels of experience using data from just one IMU placed on the knee. A spline interpolation was used in the pre-processing stage to ensure that each sample was the same length, following the division of each signal into segments of equal size and 50% overlap.