# Secure and Privacy-Respecting Documentation for Interactive Manufacturing and Quality Assurance
## 1 Introduction  
In recent years, integrating smart and intelligent manufacturing processes into production systems has become an increasingly important challenge. A multitude of research has been conducted regarding interdisciplinary and human-centric approaches to achieve this goal [1, 2, 3, 4, 5]. One realization emerging in the wake of this effort is that many different technologies and methodologies, e.g., human-machine interaction, networking, artificial intelligence (AI), smart sensing and data acquisition, have to be considered. Smart production facilities of the future will consist of networked, AI-assisted manufacturing environments in which humans will act and cooperate with the available assistance systems [6]. Increasing AI capabilities will make human work more flexible and more proactively supported by intelligent machines. A multitude of different sensors will monitor production processes for quality assurance reasons, among others, and provide a comprehensive digital twin of all production processes. In order to take human activities into account both for human-machine interaction and for proactive assistance and process mapping, production environments of the future need to be able to perceive humans and understand their actions. Possible applications for assistance systems of this kind can be found wherever humans are the central execution agency of the production process. Despite increasing automation, assembly activities and quality assurance are still carried out by humans in many companies and in many areas of production even today. In particular, it can be observed that the human work process is adapted to the conditions of the surrounding automation. Instead of attaching importance to an intuitive procedure that is familiar to the human being, the worker is forced, for example, to check components for defects in short cycle times and then to document them on a PC or sometimes on a portable tablet [7]. As a result, the transfer of detected defects is forgotten or happens inaccurately. Error slippage leads to poor quality or high rework costs. Simple gesture recognition systems already make it possible to acquire documentation directly on the component by indicating and classifying detected defects by pointing with the finger [8]. As a result, additional documentation efforts are eliminated, error slips are minimized by direct annotation on the component, and defect positions can be precisely referenced on the digital twin of the component [3]. Besides error documentation, smart assistance functions have been examined in the context of numerous other applications as well. Relevant examples include advanced inspection and quality assurance systems [9; 10; 11], effective induction and training methods [12; 13; 14], as well as assistance systems that aim to establish inclusive [15], non-discriminatory [16] and immersive [17] work environments. In all of these cases, the detection of the worker and associated innovative interaction modalities offer the opportunity to develop assistance functions that optimally support human performance due to intuitive operability. Gesture recognition in combination with speech offers a form of communication that people use subconsciously and intuitively in everyday life. These techniques can be used advantageously in many applications of human-machine communication, especially in production. Beyond innovations in human-machine interaction, video-based capturing of workers offers previously unconsidered possibilities for making production more flexible, safer and more reliable. It allows assistance systems to perceive and interpret the activities of the worker along with their context of action. Assembly activities no longer have to be explicitly confirmed, e.g., by pressing a button, if the respective work steps can be perceived and understood by the smart assembly environment [2].