## 2 Related Work
### Natural Interactions
Wearables provide more accurate and reliable results and can give haptic feedback, which gives them an advantage over visual systems. In addition, there is a third category for systems that combine both approaches. Nowadays, sensors and cameras are preferred for hand tracking as they offer greater freedom of movement [76]. Another form of gesture-based NUIs is whole-body gesture recognition, which captures body movements for more immersive interactions, as often seen in VR or AR applications [77,78]. Similar to hand gesture recognition, cameras and depth sensors are often used to recognize full-body gestures. Sensors are also used in the form of suits that track body movements via various sensors such as accelerometers, gyroscopes, and magnetometers that detect limb movements, posture, and orientation [44]. On a larger scale, ultrasonic or radar sensors are used to recognize the position and movement of the body in space [75], as well as computer vision algorithms that enable the recognition of whole-body gestures through video analysis [79,80]. Facial gesture recognition focuses on recognizing facial expressions as input so that devices can respond to emotions or gaze, which is common in accessibility technologies [81]. Finally, multimodal gestures combine different types of inputs, such as hand, voice, and gaze inputs, to create a more intuitive and efficient interaction experience [82]. Technologies for recording electrical biosignals, such as brain-machine interfaces (BCIs), which are based on electroencephalography (EEG) and electromyography (EMG), are often referred to under the collective term ExG [53]. With BCIs, it is possible to send commands to a computer using only the power of thought. A standard non-invasive BCI device that does not require brain implants is usually available in the form of an EEG headset or a VR headset with EEG sensors. The design of the device has evolved so that today smaller devices are produced in the form of EEG headbands that measure brain activity and other data such as blood flow in the brain, while the mapping of the collected signals to the user's gestures is complex and relies heavily on a learned model [83]. EMG signals are used for various purposes, independently or in combination with other sensors, for example, for interpreting the user's intentions when interacting with virtual objects [84], for device management [85,86], for analyzing muscle mass during rehabilitation [87], or for speech recognition [88,89]. The application of electrical interfaces to capture biosignals faces a number of problems, such as the detection of biological signals and the difficulty of precise localization of activity. Biological signals are variable, and errors in detection and classification increase the error rate, resulting in a lower speed of information processing compared to classical interaction methods. An additional problem is the so-called \"midas touch\" effect, which has already been mentioned for UIs with eye tracking. In addition to the technical challenges, there are also ethical problems, as, for example, the EEG data used during processing can reveal personal information of the user [53]. The scope of such interfaces in mobile and XR applications is limited due to performance accuracy as well as stability between sessions [83], but they are essential in certain areas such as rehabilitation, prosthetics, and assistive technologies, especially for people with severe disabilities for whom such interfaces are the only possible form of interaction with systems [53]. Achieving naturalness in every context of use and for all users is a challenge. While gestures, speech, and touch play an important role in many NUIs, they only feel truly natural to users if they match their abilities and specific usage scenarios as well as the context of the application and gestures. Given the technology available at the beginning of the 21 st century, it is almost impossible to create a 3D UI that feels natural to all users.