## 4 The Human Body Tracking and Joint Recognition  
Precise recognition and localization of joints are vital in gestural interaction. Several technologies and approaches exist, providing the user with tracking capabilities. According to [PERSON] and [PERSON] [34], human motion tracking technologies can be divided into non-visual, visual, and robot-aided tracking.  
In non-visual tracking, the human motion and joints are mapped via various sensors placed onto the human body; typically, MEMS IMU or IMMU devices are used. These sensors are usually a part of a suit or so-called \"data glove\", which must be worn by the user [35; 36; 37; 38; 39]. Although these systems are precise and relatively cheap, they suffer from many shortcomings, such as the need for the user to wear the sensory suit, which may be uncomfortable and often calibrated for the specific user. The sensors on the suit may require extensive cabling, which can limit the user's range of motion. The sensors themselves may suffer from several issues, such as null bias error, scale factor error, noise, or interference. Due to this, the general application focus shifted towards the use of visual-based human body tracking.  
This approach uses optical sensors, such as RGB, IR, or depth cameras to extract the user's spatial position. Complex image processing algorithms are then applied to pinpoint the precise position and orientation of human joints. These sensors do not require any equipment that the user needs to wear, thus not limiting him in any way. They can be easily calibrated to the specific user, which greatly improves their flexibility. Today's camera technologies and image processing algorithms make them fast and accurate and relatively cheap. These attributes made these sensors popular and widely used in various commercial applications, mainly in HCI and video-gaming industry. However, visual-based sensors still have multiple drawbacks, such as the sensitivity to the lighting conditions and worsened tracking capabilities when the human body is occluded. The most widely recognizable sensor for visual body tracking is the Microsoft Kinect, released in 2010, initially intended for video gaming. However, due to its capabilities, the sensor became widely used in other applications. The Kinect's principle of body tracking relies on capturing the depth data from the depth sensors and then applying their image processing methods to produce a so-called \"skeletal stream\" representing the human figure. The first iteration of the sensors used an IR projection pattern to acquire the depth data. The latter versions used the so-called \"time-of-flight\" technology or TOF. According to [PERSON] et al. [40], body part classification (BPC) and offset joint regression (OJR) algorithms, specially developed for Microsoft Kinect, are used to determine the parts of the human body. Following the success of Microsoft Kinect, other similar sensors were released, such as Intel RealSense or ASUS Xtion.  
Another widely popular vision-based motion tracking sensor is the Leap Motion controller. This compact device was specially designed to track the human hands and arms at very high precision and accuracy. Alongside gaming and VR applications, the controller's design was initially meant to replace conventional peripheral devices, such as a mouse and keyboard, and provide a more sophisticated and natural HCI. The Leap Motion controller uses two IR cameras, capturing emitted light from three LEDs with a wavelength of 850 nm. The depth data are acquired by applying unique algorithms on the raw data, consisting of infrared brightness values and the calibration data [41]. According to the manufacturer, the accuracy of position estimation is around \(\pm\)0.01 mm. However, several studies show [42; 43] that this is highly dependent on the conditions.