Today's digital world is characterized by the need to collect and evaluate data about the real world. The term Internet of things describes, for example, the major trend towards building a global infrastructure of information societies that makes it possible to network physical and virtual objects and to make them work together using information and communication technologies. Another major trend is augmented reality, in which computers are supposed to help people to see and understand the world better. A current example is the head-up displays in cars, where pieces of information (navigation instructions, speed, etc.) are superimposed on the driver's field of vision. To enable this, computers rely on sensor systems that provide information from the real world as interpretable content. Today, large corporations fall back on a variety of methods to obtain and evaluate information about the world. These methods include services such as speech recognition, image recognition, text analysis or maps. An entire industry has specialized in using intelligent and learning algorithms to gather and understand data about the world. However, an essential source of data has not yet been economically accessible: (human) movement. This does not mean the location of a person, which is already known via GPS, but the exact 3D data of human kinematics. These data not only provide important information about physical fitness, health status and athletic abilities, they also provide information for virtual realities or allow the interpretation of behavior. These data, as a source of data for interaction with the digital world, are a field that has barely been tapped so far, because previous technologies have not been able to obtain the data with a proportional and feasible effort. 3D acquisition and interpretation of human motion has therefore been a pure discipline of science so far. It has built up enormous methodological knowledge in the last decades, but it is still dominated by expensive sensor-based systems.