## 4 Discussion
As well, the \(z\) coordinate marker information provides an additional feature per landmark which may allow the model to learn additional patterns. As the system we used was comprised of four cameras, the inaccuracies of the DLC model were reduced as the marker location was triangulated between cameras. The 2D system is unable to use this method of triangulation to calculate the average marker center because the center of the marker gets expressed relative to the viewing angle in a 2D plane. For the 3D system, the added spatial data contained within the extra features resulted in improved accuracy. [PERSON] et al. determined an error of 14-20\({}^{\circ}\) for FE and 9.1-11\({}^{\circ}\) for RUD depending on arm orientation [18]. When comparing [PERSON]'s result to ours, the current study demonstrated similar accuracy (12.2\({}^{\circ}\)) for FE and an improved accuracy for RUD (6.7\({}^{\circ}\)). Furthermore, the accuracy of 3 DML improved FE error to 7.2\({}^{\circ}\) and RUD error to 5.5\({}^{\circ}\). Compared to a study that measured gait kinematics using the commercially available Theia MMC system, we observed similar results. In this instance, the foot was selected for comparison because it most closely replicates the ROM of a wrist. [PERSON] et al. found an ankle plantar flexion/dorsiflexion RMSE of 6.7\({}^{\circ}\) and an abduction/adduction RMSE of 11.6\({}^{\circ}\)[16]. Our measures compare favorably, with MAEs of 7.2 \(\pm\) 0.9\({}^{\circ}\) for FE and 5.5\({}^{\circ}\) for RUD. The elbow markers displayed low feature importance in both the 2 DML and 3 DML methods, indicating that there is a possibility the models could achieve similar results without the elbow features. This is a key piece of information for the deployment of this method as fewer markers would move this closer to being a truly markerless system. Additionally, removing the elbow markers would allow for different camera positioning that can focus on only the wrist and hand markers and potentially achieve higher accuracy in tracking those markers. By using a grid search for multiple types of regression algorithms, we were able to determine which model provided the highest accuracy for the two individual ROMs, as well as the overall best algorithm. The support vector regressor showed promise as an accurate model, slightly outperforming the neural network for FE and RUD accuracy in shuffle 3 of 3 DML and RUD accuracy in shuffle 4 of 3 DML. However, the neural network provided better results across the individual shuffles for the lowest mean MAE in FE and RUD accuracy, except for shuffle 3 in 3 DML. Though not statistically better, the neural network outperformed all models in every shuffle of 2 DML for both FE and RUD. It is evident from both Tables 3 and 5 that the RUD angles were predicted with higher accuracy compared to the FE angles. The FE angle was consistently less accurate in both 2D and 3D applications, regardless of the model. As the FE ROM was larger than the RUD ROM, errors that occurred in the extreme values may have been larger for FE compared to RUD predictions. The higher error in FE predictions aligns with the trends commonly seen in the studies [18; 41; 49; 50] which show that FE quantification poses a greater challenge. A multi-camera system and a single-camera system for CV-generated data were both assessed for FE and RUD angle prediction. While the 2 DML model was less accurate, it does pave the way for more analyses on how to use a single-camera for 3D wrist motion prediction. Two-dimensional systems have been shown to be an effective way to analyze an angle as long as the plane of movement is parallel to the FOV [14, 31, 51]. The camera used for the 2 DML analysis was placed in an oblique position between the frontal and right-side cameras. This demonstrates that there is a possibility of achieving accurate angles in multiple planes even when the plane of movement is not parallel to the FOV. With this knowledge, it may be possible to develop a system that can predict wrist kinematics using a single-camera setup.