## 2 Materials and Methods
### Materials and Software  
* Optoelectronic 3D motion capture  
Lower body 3D kinematics were collected with a 10-camera Vicon motion capture system (VICON, Oxford Metrics, Oxford, UK) (100 Hz), using the International Society of Biomechanics (ISB) lower limb marker model (26 markers) [25]. The 3D knee and hip flexion/extension joint angles were quantified via VICON Nexus software (version 2.12.1) using Euler Angles. * Two-dimensional deep-learning-based motion capture  
Two-dimensional (2D) pose estimation was performed with DeepLabCut (version 2.3.7), an open-source deep learning python toolbox ([[https://github.com/DeepLabCut](https://github.com/DeepLabCut)]([https://github.com/DeepLabCut](https://github.com/DeepLabCut)) (accessed on 10 October 2023)) [21; 22]. Anatomical landmarks (i.e., ankle, knee, hip and shoulder) were tracked in sagittal video recordings (Figure 2). The resolution of the video recordings was 644 \(\times\) 486 pixels when recorded at the M2 OCEAN laboratory and 480 \(\times\) 640 pixels when recorded at the GRAIL. The frame rate of the video recordings was at both sites 50 frames per second. Separate DeepLabCut neural networks per joint for the left and right sides were created. The pretrained MPII human model of the DeepLabCut toolbox (ResNet101) was used to retrieve 2D coordinates of these anatomical landmarks:  
* With an additional 30 manually labeled frames from eight videos of left knee JPR test trials of six different children (network 1),
* with an additional 25 manually labeled frames from six videos of right knee JPR test trials of five different children (network 2),
* with an additional 28 manually labeled frames from six videos of left hip JPR test trials of six different children (network 3) and  
Figure 1: The passive-ipsilateral JPR task for the knee and hip. (**A**) Experience: from the resting position (Knee: 90\({}^{\circ}\) of knee flexion, Hip: 70\({}^{\circ}\) of hip flexion), the examiner moved the child’s limb 30\({}^{\circ}\) to knee extension or 20\({}^{\circ}\) to hip flexion, using an inclinometer distally attached to the moving segment (knee: lower leg, hip: upper leg), approximately perpendicular to the flexion-extension axis. (**B**) Memory: after experiencing and memorizing this joint position for five seconds, the child’s limb was passively returned to the neutral start position. (**C**) Reproduction: afterward, the examiner moved the ipsilateral limb back into the same range and the child was asked to re-identify the target joint position (Knee: 60\({}^{\circ}\) of knee flexion, Hip: 90\({}^{\circ}\) of hip flexion) as accurately as possible by pressing a button synchronized to motion capture software. with an additional 26 manually labeled frames from nine videos of hip JPR test trials of six different children (network 4)  
The labeled frames are a combination of frames from videos of the study and pilot participants (Tables 1 and 2, Figure 3). These videos were used only for network training and were excluded from statistical analyses. If one or more videos of a child were used for additional labeling to extend the neural network, all videos of that child were excluded from statistical analyses (Tables S1 and S2). Frames for labeling were manually selected in order to select frames with a variety of appearances (e.g., clothing and posture of the participant and examiner, background, light conditions) within the datasets. The labeled frames were randomly split into either a training or test set (95% training dataset, 5% test dataset). The knee networks were trained up to 520,000 iterations and the hip networks were trained up to 500,000 iterations, both with a batch size of eight. Only trials with confident predictions of the coordinates of the anatomical landmarks were included in the analyses (i.e., the likelihood of confident predictions > p-cut off of 0.8).