## 1 Introduction
identified three main use cases for HMC: (1) surveillance of crowds and their behavior, (2) controlling software through specific movements or gestures or controlling virtual characters in the entertainment industry such as in movies, and (3) analysis of motion for diagnostics, for example in orthopedic patients or performance improvements in athletes [2]. While use case (1) focuses on tracking multiple subjects, (2) and (3) focus on capturing body motion of a single subject and thus require tracking of several parts of the human body. Especially use case (3) offers several applications of HMC, which are often limited to professional use cases such as gait analysis [24] or sports applications [7] due to the lack of a reliable, accessible, and low-priced solution in on-field settings. In the sports and health sector, the usage of mobile applications has significantly increased in the past years [25; 26]. Research has shown that such apps can positively impact their user's health and lifestyle [27]. However, most fitness and health apps only allow limited tracking and analysis of motion [28]. While smartphone-based motion capture promises a lightweight and consumer-friendly motion capture and analysis, the software systems have only been evaluated to a limited extent. Moreover, research has been focused on 2D systems. Several studies have shown that in 2D-motion analysis, the reliability and validity of the kinematic measurements are dependent on the performed task, which reliability is measured, video quality, and position of the recording device [8; 13; 29; 30]. Especially the camera position influences the accuracy of tracked joint angles. A slightly different viewing angle already distorts the result of the joint angle, which is why triangulation with multiple devices is often performed to overcome the limitations of monocular camera setups [31]. Among mobile 2D motion capture systems, the OpenPose software is widely used and evaluated in several studies [19; 23; 30; 32; 33; 34; 35; 36; 37]. The results show that OpenPose delivers accurate biomechanical measurements, especially when tracking the joint trajectories. However, the compared joint angles differed significantly from the gold standard systems. [PERSON] et al. measured up to 9.9 degrees difference in the minima and maxima of the tracked joint angles during gait analysis [35], [PERSON] et al. experienced deviations of more than 40 mm in their study [37]. The measuerements can by improved by using multiple devices to calculate the body position in 3D as in the study by [PERSON] et al. [30]. Mobile 2D motion capture systems have been recently complemented by 3D motion capture algorithms, which estimate the 3D joint positions based on 2D monocular video data [20; 38; 39; 40; 41; 42]. They detect and calculate the body's joint coordinates in all three movement planes, making the motion capture more robust against the camera's viewing angle. Mobile 3D motion capture frameworks could overcome the limitations of 2D motion capture systems. Some of the 3D motion capture frameworks use additional smartphone sensors such as integrated accelerometers to determine the smartphone's position or depth sensors such as the integrated Light Detection and Ranging (LiDAR) depth sensor to additionally enhance the position detection of the human body [20; 38; 39]. The LiDAR data can be used to create a dense depth map from an RGB image through depth completion [43]. Among the most well-known mobile 3D motion capture systems is Apple ARKit, which released a body-tracking feature as part of their Software Development Kit (SDK) for developers in 2019 [20]. In contrast to other 3D motion capture frameworks, ARKit is free and easy to use, and widely accessible. On the latest devices, it uses the smartphone's IMUs and integrated LiDAR sensor to improve the measurements, promising enhanced mobile motion capture. However, only a few scientific studies have evaluated the accuracy of mobile 3D motion capture frameworks and ARKit in particular.