# Design and Validation of Rule-Based Expert System by Using Kinect V2 for Real-Time Athlete Support
## 1 Introduction  
Simultaneous monitoring of movements in the training process in sports such as bodybuilding and weightlifting is a necessity for athletes to train healthily. This allows the athlete to train effectively without the risk of injury until motor skills related to movement develop. Athletes and trainers are looking for ways to train most effectively without causing injury. This process is carried out within the framework of training programs as in all sport branches. Visual training programs designed for more accurate and safe training use visual perception and motor tasks based on it [1]. By using these programs, potential injury risks may decrease and sensory processes and motor movements may become faster and more accurate [2]. In the literature, there are studies about the optimization of sports technique and the use of more effective training mechanisms and computerized vision and human-machine interaction in sports on rehabilitation processes [3]. The methods developed for this purpose are generally divided into two groups as observational and direct measurement [4]. Data collection in the observation method is based on subjective observations or on a simple estimation of angles provided from videos or images [5]. This method does not restrict the movements of the athletes and does not disturb the athlete [6]. However, it is costly to place multiple cameras for information in suitable locations where visibilityis not obstructed in gyms [7]. In the direct measurement method, data is collected through sensors attached to the body of the athlete. There are three main methods in this group: optical, mechanical, and magnetic [8; 9; 10]. In an optical system, the correct placement of markers is crucial. The fact that a marker is misplaced, slipped, or not seen by the system before training can lead to incorrect results in the monitoring process [11; 12]. Magnetic and mechanical systems can restrict the movement of the athlete due to the need for cabling [13; 14]. Mechanical systems have to be calibrated frequently because of sweating or bumping during training [15]. These methods cannot frequently be used in a real gym environment due to the time required for calibration--setup time and preparation (sensor placement, installation, cabling, etc.). An alternative to all this is Kinect, a second generation RGB-D type sensor (2014) which is notable for its ease of setup and usage. This sensor includes an RGB camera, an infrared camera, and a microphone mechanism. Kinect uses its own proprietary algorithm to create 3D movements to enable monitoring movements without the need for any special equipment or markers [16]. Studies on the ability to monitor body movements during static and dynamic balance tests of Kinect have shown that this sensor has a wider accuracy range compared to video movements capture systems [16; 17]. In comparison with systems with gold standards (\(\geq\)99% accuracy), it is stated that this sensor could be used in biomedical, sports, and rehabilitation fields [18; 19]. The methods used in the literature to monitor and evaluate human movement are divided into two parts: template-based and pose-based [20; 21; 22]. In template-based methods, a movement or movement group is recorded and analyzed sequentially to form a model [23; 24]. The monitored movements are used to define the movement or activity through this model. In this approach, results are obtained depending on whether the monitored movement is defined by the template or not. The pose-based approach does not require specific examples and trained models [25; 26]. Instead, a key dynamic set of rules for the movement is defined, indicating how much the performed movement with the given feedback is similar to the one that is defined [27; 28]. When these methods are used in the gym environment, they give information about what the movement is, but cannot give any correction data about how the distortions in movement can be corrected.