## 6 Conclusions  
This research evaluated mobile 3D motion capture based on the example of ARKit, Apple's framework for smartphone-based 3D motion capture. In contrast to existing monocular motion capture software, ARKit detects the human body in a 3-dimensional space instead of only two dimensions and augments its results by using smartphone sensor data such as IMU or depth data from the integrated LiDAR sensor. Our laboratory experiment, including ten participants, investigated ARKit's accuracy and influencing factors in eight body-weight exercises and compared it to the Vicon system, a gold standard for human motion capture. Our results provide evidence that mobile 3D motion capture frameworks can track the motion's progression with reasonable accuracy but with relatively high mean absolute error rates. The accuracy mainly depends on two factors: the visibility of the joints of interest and the observed motion. In contrast to 2D systems, the 3D motion capture framework exposed certain robustness against the positioning of the camera. However, similar limitations regarding the tracking of poorly visible joints remain. Mobile 3D motion capture frameworks are promising and lightweight mobile technologies which could enable new use cases for human-computer interaction through motion or application in health and medical fields. Their limitations, especially regarding the relatively high error rates compared to the gold standard system, need to be considered for each use case. Conceptualization, L.M.R., M.K., T.F. and S.M.J. ; methodology, L.M.R., M.K. and S.M.J. ; software, L.M.R. ; validation, L.M.R., M.K., T.F. and S.M.J. ; formal analysis, M.K. and L.M.R. ; investigation, L.M.R. and T.F. ; resources, L.M.R. and S.M.J. ; data curation, M.K. ; writing--original draft preparation, L.M.R., M.K. and T.F. ; writing--review and editing, L.M.R., M.K. and S.M.J. ; visualization, L.M.R. and M.K. ; supervision, L.M.R. and S.M.J. ; project administration, L.M.R. ; funding acquisition, L.M.R. All authors have read and agreed to the published version of the manuscript. This work was supported by a grant from Software Campus through the German Federal Ministry of Education and Research, grant number 01 IS17049. The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Ethics Committee of the Technical University of Munich (Proposal 515/21 S on 19 August 2021). All participants were informed about the aims of the study and gave their consent about the publication of the anonymized data. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Written informed consent has been obtained from the subjects to publish this paper. All data is available on Zenodo [49]. We want to thank [PERSON] for his support during the planning and execution of the study as well as the participants of the study. The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. The following abbreviations are used in this manuscript: \begin{tabular}{l l} AOI & Angles of Interest \\ EMS & Electromagnetics Measurement Systems \\ FL & Front Lunge \\ HMC & Human Motion Capture \\ IPS & Image Processing Systems \\ IMU & Inertial Measurement Unit \\ JJ & Jumping Jacks \\ LAR & Lateral Arm Raise \\ LEC & Leg Extension Crunch \\ LE & Left Elbow \\ LH & Left Hip \\ LK & Left Knee \\ LS & Left Shoulder \\ MAE & Mean Absolute Error \\ ME & Mean Error \\ OMS & Optoelectronic Measurement Systems \\ PCC & Pearson Correlation Coefficient \\ RE & Right Elbow \\ RF & Reverse Fly \\ RH & Right Hip \\ RK & Right Knee \\ RS & Right Shoulder \\ S & Squat \\ SDK & Software Development Kit \\ SS & Side Squat \\ \end{tabular}   
Figure 14: Distributions of the individual factors of the ME on the logarithmic scale used in the Welch ANOVA analysis.