## 4 Discussion  
The aim of this study was to validate two-dimensional deep-learning-based motion capture (DeepLabCut) with respect to laboratory-based optoelectronic three-dimensional motion capture (VICON, gold standard) to assess proprioceptive function (joint position sense) by measuring the JRE during knee and hip JPR tests in typically developing children. We validated measuring knee and hip systematic JREs using DeepLabCut with respect to VICON, but also validated measuring the best absolute JREs using DeepLabCut, in order to also validate a clinically applicable outcome measure (where over or undershooting is not important). Until now, no previous study has reported the validity of a deep-learning-based motion capture system for the assessment of proprioception by measuring the JRE during knee and hip JPR tests. Deep-learning-based motion capture techniques (such as OpenPose, Theia3D, Kinect and DeepLabCut) have already been validated to measure joint kinematics and spatiotemporal parameters with respect to a marker-based motion capture system during walking, throwing or jumping in children with cerebral palsy [26] and healthy adults [27; 28; 29; 30; 31; 32]. The average Root Mean Square (RMS) errors of the captured sagittal knee and hip joint angles between markerless tracking and marker-based tracking ranged from 3.2 to 5.7 degrees and from 3.9 to 11.0 degrees, respectively, during walking. These differences between joint angles measured with the two systems are higher than the average absolute differences in systematic JREs measured with the two systems in the current study (knee: 1.2 degrees hip: 1.7 degrees). Greater differences between the systems during walking compared to JPR tests could be explained by the greater movement speed of the body segments during walking. Greater movement speed increases the probability of image blurring, which may lead to inaccurate markerless tracking of the body landmarks [33]. Furthermore, the differences in JREs measured with the two systems are negligible because the averages of the absolute JRE differences between the systems (knee: 1.2 degrees (systematic) and 0.9 degrees (best absolute), hip: 1.7 degrees (systematic) and 1.2 degrees (best absolute)) are smaller than the intersession standard error of measurement (SEM) values for the knee and hip JRE measured with VICON, which were previously determined in our sample of typically developing children (knee: 2.26 degrees, hip: 2.03 degrees) (submitted for publication; unpublished [23]). Further research is needed to assess the reliability of measuring the JRE with DeepLabCut. In order to use DeepLabcut to assess proprioceptive function, it is important to unravel the potential cause of the proportional bias, which was present in the hip Bland-Altman plot. The difference between the hip JRE measured with VICON and DeepLabCut was greater when the mean JRE of both systems deviated more from zero. More specifically, the difference between the hip JRE measured with VICON and DeepLabCut greater than two degrees (=hip SEM) often corresponded to a relatively large VICON JRE of at least three degrees. However, large VICON JREs did not always correspond to large differences between the hip JRE measured with VICON and DeepLabCut. This means that DeepLabCut JREs were not measured accurately in some children, but not in all children with relatively large VICON JREs of at least three degrees. A possible explanation could be that the DeepLabCut neural networks were not optimally trained to accurately predict the coordinates of the anatomical landmarks (especially the shoulder joint when wearing a loose-fitted shirt, which could have caused the presence of a proportional bias only in the hip Bland-Altman plot), which are used to determine joint angles, in some children with relatively large deviations of the reproduction angle from the target angle (i.e., large JREs). The presence of a proportional bias may be prevented by extending the neural network models with more labeled frames during reproduction during a trial with a large JRE.