## 3 Results and Discussion
### Possibilities and Limitations  
This study has introduced the combination of two promising methods (dGNSS and PosEst) for performance analysis in ski jumping, which have not been extensively used in recent ski jumping research. The good agreement between the methods for take-off and the early flight phase, and the analytic possibilities that this sensor fusion can provide, have been presented. The major strength of using dGNSS in ski jumping is the ability to measure the kinematic variables (and their kinetic derivations) from start of the in-run to the landing in a consistent and relatively resource-conservative manner, possibly with better accuracy than, for example, IMU-based estimations of the trajectory [26; 27; 29; 30; 31]. Since the dGNSS method allows estimation of the external forces, velocity and position instantaneously from start of the in-run to the landing, with good global accuracy, the method can offer a holistic assessment of performance and its underlying factors. Instantaneous tracking of velocity and forces allows not only description of how these parameters develop over time but also an explanation of how these interact and affect performance, albeit with limited validity during the take-off phase. Hence, the dGNSS method alone could help an individual or coach to better understand the reasons behind a given performance. A limitation to this method is that it cannot be used in competition, as the dGNSS has to be mounted on the ski jumper. It is also important to notice that the antenna of this dGNSS was placed on the helmet and is used as a point mass representation of the athlete, which is a reasonable assumption in the in-run and flight phase, but during take-off and early flight phase the head motion substantially deviates from the CoM motion. Mounting of the dGNSS system closer to the pelvic region may partly solve this issue. A complementary method should be used to accurately assess the CoM motion to provide data from what is to date considered to be the most important phase of ski jumping. The usage of video to analyse the take-off is not new [2; 17; 18; 19; 20; 21; 22], but no research published to date has used a markerless, video-based pose estimation using computer vision. Usage of a PosEst method can reduce the biggest limitation of video analysis, i.e., the time used on data processing. The PosEst was used to assess the take-off and early flight phase from one camera and it was demonstrated that the PosEst method can reconstruct the segment motion of the athlete in the sagittal plane along with CoM position, velocity and estimations of the external forces. This can be done without interfering with the athlete and is thus well suited to competition analysis. The camera-based PosEst method can be applied at any point along the trajectory but may be most important in the take-off phase, which is considered to be the most important phase of ski jumping [1; 2; 3]. This method also becomes more and more promising as camera technology develops, enabling filming in high resolution with a simultaneous high frame rate. Such a system could also further be improved by an automatic object detection and crop procedure. Covering a larger part of the in-run, jump, flight phase and landing could be achieved by synchronizing several cameras. Calibration of the 3-D space covered by multiple cameras is another important but solvable challenge. Used together, these two methods cover most of the motion analysis features of ski jumping, except for the ski motion data which are difficult to obtain in the sagittal plane but are nonetheless important in ski jumping [53]. A possible way to capture both the pitch, roll and yaw of the skis could be to place an IMU on each ski and time-synchronise these with the dGNSS and video. IMUs may also be valuable for obtaining kinetic parameters like angular velocities and acceleration of limb segments for the complete ski jump [28]. An interesting feature that could be assessed using the presented sensor fusion is the onset of the constant flight phase.