# Design and Validation of Rule-Based Expert System by Using Kinect V2 for Real-Time Athlete Support
## 1 Introduction
When the studies using Kinect in the field of sports and rehabilitation were examined, it was observed that in the pose monitoring studies of [PERSON] et al., movement distortion was shown only in the respective joints by comparing the results they obtained during training with the pose template they created before and finding the movement matching rate [29], that [PERSON] et al., gave feedback by manually examining the results of the movement follow-up after training [30], and that [PERSON] et al., performed kinematic analyses of handball throwing poses after training [31]. Furthermore, [PERSON] et al., investigate the performance monitoring and user satisfaction through the similarity of movement with the pose monitoring-based system they had developed for supervised training at home [32]. However, it has been observed that all these systems were unable to give users simultaneous feedback on how to train more effectively and efficiently by correcting the wrong movement. In their studies in the field of sports and rehabilitation for hemophilia patients, [PERSON] et al., analyzed and conducted performance analysis on similarity with Kinect through dynamic time warping (DTW) by examining movements affecting single and multiple joints through posing follow-up [33], [PERSON] et al., designed a system for wheelchair users and combined physical skills with entertainment [34], [PERSON] et al., analyzed user performance through movement similarity by processing pose data from tai chi exercises obtained with Kinect through DTW [35] while, on the other hand, [PERSON] aimed to help knee rehabilitation with a bike racing game he developed using Kinect [36]. However, it was seen that these systems were limited only to patients in the area where the study was conducted or with similarity in movement, and that they are not suitable in terms of movement accuracy for use in sports gym environment and weight training with isotonic movements. In an another remarkable study, a recurrent self-organization structure made by [PERSON] et al., was examined for three different weightlifting exercises and it attempted to give simultaneous feedback to the user [37]. However,also in this study, the effect of the system on physical-cognitive development with its ability to increase training efficiency was not tested for long-term use. In addition to these studies, there are also systems that perform posture and pose analysis by using Kinect in the treatment of Parkinson by reaching the shapes and in the treatment of balance disorder by following walking on a straight line [38; 39; 40]; also in these studies, however, it was shown that the abovementioned deficiencies were still present. When all these mentioned studies are examined, the 10 main features that should be considered in an athlete support system can be listed as follows. These are:  
* Simultaneously work (S);
* Determination of movement accuracy (MA);
* Giving feedback for movement correction (F);
* Skeleton extraction (SE);
* Usability in training (UT);
* Usability in rehabilitation (UR);
* Being oriented towards physical ability (PA);
* Being oriented towards cognitive ability (CA);
* To be able to make pose analysis (POA);
* The ability to perform movement model analysis (MMA). All these features can be simultaneously used during training or rehabilitation processes; in particular, it may be useful for beginner athletes to perform healthy training in sports such as body building and weightlifting, to perform an instant performance evaluation until the physical and cognitive skills of the athlete related to movement develop, to prevent the injury process by intervening during the training when necessary, or to increase the efficiency of rehabilitation processes. Based on these elements, a new athlete support system is proposed in our study using Kinect V2 and Expert System as shown in Figure 1.  
In addition, a comparative summary of these studies and the characteristics of our study are presented in Table 1.  
Figure 1: Example view of the proposed system.