## 2 Materials and Methods
### Video Based PosEst Method  
The PosEst system estimated the \(x\) and \(y\) coordinates of 16 body parts (i.e., head top, upper neck, shoulders, elbows, wrists, upper chest, right/mid/left pelvis, knees, and ankles) in the video frames in a frame-by-frame manner using a state-of-the-art convolutional neural network (ConvNet) for high-precision pose estimation. For further technical details about ConvNet, the reader is referred to [41]. To perform pose estimations of the ski jumpers, the PosEst was trained, validated and tested on 5064 randomly selected video frames (i.e., 3686 (73%) for training, 365 (7%) for validation, and 1013 (20%) for testing) from an internal database of jumps recorded between January 2014 and June 2019. All 5064 video frames were manually annotated by three experts to define the positions of the body parts, and the PosEst was fine-tuned against these. The video data was captured with one camera (Camera: Blackmagic Pocket Cinema Camera, Lens: Sigma 18-35 mm). The camera was placed 7 m into the flight (relative to the in-run edge) and at a 28 m distance away from the trajectory. The camera view is shown in Figure 1.  
The camera filmed in 4K (4096\(\times\)2016 pixels) at 60 Hz with an electronic shutter speed of 1000 s\({}^{-1}\). The camera was horizontally levelled and slightly angled on the trajectory to capture as much as possible of the flight and the take-off (6.5 m prior to take-off to 22 m after). The image space was calibrated using the ski jumpers segment lengths, measured with a measuring tape prior to testing. The image calibration was conducted by first rotating the image plane in such way that the average segment length of the leg, thigh and arm of the ski jumpers in the 16 jumps remained constant during the flight. Second, calibration was achieved from the image space in pixels to the object space in meters using the athletes' segment lengths.  
The PosEst system required the whole of the ski jumper to be visible in the picture. Each video was trimmed to start at the first, and end at the last, picture where the complete ski jumper was in the picture. A standardised function was created to automatically crop a 500\(\times\)400 pixel frame around the ski jumper and move frame by frame with the ski jumper. Both the size of the frame and the automatic cropping function were defined manually in the post-processing and the same frame and function were used for all jumps. The camera  
Figure 1: Camera view for the experiment together with examples of the cropped and tracked windows during the in-run, take-off and flight phase.  
view, together with examples of how the pictures were cropped and tracked in the in-run, take-off and flight are shown in Figure 1. The area analysed for all jumpers was from \(-5\) to \(20\,\mathrm{m}\) relative to the jump edge. This experiment had the benefit of not having much human interference in the camera view during the experiment. However, this will not always be the case when filming and it can also be difficult to control. Even if the system is trained to recognise the ski jumper, the appearance of other people or objects in the background can compromise the intentional track. By cropping the picture before tracking one can ensure that the ski jumper is the only human in the tracked picture, hence one will have a less chance of losing data. As seen in Figure 1, another person is present in the lower part of the camera view but is not visible in the cropped pictures and does not interfere in any way. Even if the PosEst located the ski jumper as a 3D object in the picture, the landmarks on the side furthest away had a higher uncertainty. Thus, this study only used the landmarks on the right side for a 2D analysis. This was seen as sufficient, as the movement of the ski jumper primarily occurred in the sagittal plane [23]. CoM was calculated from the PosEst-based joint centre landmarks, the anthropocentric parameters were according to Zatsiorsky with De Leva [42] adjustments, and they were adjusted for the mass of helmet, boots and skis [43].