## 1 Introduction  
Human Motion Capture (HMC) is a highly researched field and covers the detection of all kinds of human motion, including movements of the whole body or smaller parts such as the face or hands [1]. In their publications from 2001 and 2006, [PERSON] et al. found more than 450 publications researching vision-based HMC and analysis [1, 2], not considering HMC using different technologies such as inertial or magnetic sensors. Traditional HMC systems are bound to an off-field setting [3, 4] and are expensive in installation and operation [5, 6], limiting their application to professional use cases. In their review of motion capture systems in 2018, [PERSON] and [PERSON] identified five types of motion capture systems: Optoelectronic Measurement Systems (OMS), Inertial Sensor Measurement Systems, Electromagnetic Measurement Systems (EMS), UltrasonicLocalization Systems (ULS), and Image Processing Systems (IPS) [7]. They introduce OMS as the gold standard for motion capture [7]. Indeed, many studies [8; 9; 10; 11; 12; 13] used OMS such as the Vicon motion capture system (Vicon, Oxford, UK) [14] or the Qualisys motion capture system (Qualisys AB, Goteborg, Sweden) [15] as reference measurement systems in their studies. OMS require multiple cameras or sensors around a subject and reflection markers on the subject's anatomical landmarks, which are then captured by the cameras or sensors. The Inertial Measurement Sensor Systems rely on Inertial Measurement Units (IMU), which are placed on the subject's body to capture motion and mapped onto a rigid-body model. Examples for IMU-based systems are the Xsens systems (Xsens Technologies B.V., Enschede, The Netherlands) [16] or Perception Neuron (Noitom Ltd., Miami, FL, USA) [17]. Through the traveling time of electromagnetic or ultrasonic waves between a tagged person and a base station, EMS and ULS track the position of the subject [7; 18]. In contrast to the other systems, these systems allow tracking one or more subjects' positions, but do not capture joint kinematics [7]. While the described systems are well-validated systems for HMC, their complex setup and costs prevent them from application in mHealth applications. With the advancements in technology and machine learning, IPS became more relevant in human motion capture. IPS rely on video input and different machine learning approaches to detect specific body landmarks and capture human motion. Among the most researched systems is Kinect (Microsoft Corp., Redmond, WA, USA), which uses a combination of an RBG-camera and infrared sensors and can capture motion in 3-dimensional space [10; 11]. However, the Kinect still requires a specialized setup for motion capture. The offer of IPS has been extended by recent advances in technology, such as enhanced sensors and processing units. These advances enable computer-vision-based motion capture on smartphones and tablets. These IPS systems offer new possibilities for HMC in mobile scenarios such as in mHealth applications. Examples for IPS software which can run on mobile devices are OpenPose (CMU, Pittsburgh, PA, USA) [19], ARKit (Apple Inc., Cupertino, CA, USA) [20], Vision (Apple Inc., Cupertino, CA, USA) [21], and TensorFlow Pose Estimate (Google, Mountain View, CA, USA) [22]. All of these IPS can be integrated into custom applications by developers. The detection of the human body and its position is realized through computer-vision algorithms, which can use Convolutional Neural Networks (CNNs) or Part Affinity Fields (PAFs) [23]. In most systems, a predefined humanoid model is then applied to estimate the shape and kinematic structure of the tracked person [2]. The algorithms deliver the joint coordinates in two or three dimensions for every video frame. [PERSON] et al.