## 1 Introduction
Anipose uses a calibration module, filtering, and an interface for visualization to generate 3D marker locations from multiple 2D videos that are analyzed using a DeepLabCut model [24]. These CV algorithms (Alphapose, OpenPose, MediaPipe, Anipose, DeepLabCut) have been used to track gait kinematics accurately with results reaching an error as low as 7.4\({}^{\circ}\) in ankle flexion-extension when compared to OMC [29; 30; 31; 32]; however, there has been limited research in wrist angle prediction [33]. The ankle was selected for comparison because it most closely replicates the ROM of a wrist. DeepLabCut (Version 2.3.3, Mathis Lab ([PERSON] & [PERSON]), Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland) was selected for our methods as it allows the tracking of user-defined locations, which is necessary to validate findings against the gold standard. On top of DeepLabCut, we used Anipose (Version 1.0.1, [PERSON] & [PERSON] [24], [[https://github.com/lambdaloop/anipose/](https://github.com/lambdaloop/anipose/)]([https://github.com/lambdaloop/anipose/](https://github.com/lambdaloop/anipose/)), accessed on 6 February 2024) allowing for 2D videos to be combined into a 3D coordinate. To the authors' knowledge, [PERSON] et al. [18] have presented the only comparison to date between marketed and markerless motion capture of upper limb kinematics including wrist flexion/extension (FE) and radial/ulnar deviation (RUD) angles. While the study demonstrated high accuracy for shoulder kinematics, high errors (up to 20\({}^{\circ}\) RMSE) were noted for wrist angles. The authors hypothesized that the larger error at the more distal segments may be due to the increased velocities of those segments or a consequence of the multibody kinematic optimization that constrains proximal segment motion more than distal segment motion. Markerless motion capture in general has shown slow progress in the upper extremity biomechanics space due to joint center tracking inaccuracies coupled with the requirement of computer vision and programming skills for system development [19]. While commercially available markerless systems require less technical expertise to deploy, they utilize proprietary datasets and algorithms making it difficult for the end user to understand the key points (e.g., joint centers) being extracted [19]. Moreover, a lack of available datasets has limited open-source markerless system development because algorithms generalize poorly if image key points differ substantially from training datasets. Publicly available, high-quality datasets that have been labeled by experts and validated by a secondary source are needed [19]. To our knowledge, open-source pose estimation algorithms such as DeepLabCut have not been applied to the human wrist to predict FE and RUD angles. Additionally, no studies have tried to improve prediction accuracy for wrist kinematics using machine learning. To further the development of open-source pose estimation algorithms, we aimed to use open-source software paired with machine learning algorithms to improve kinematic prediction accuracy. ML algorithms were chosen from past kinematic quantification research [34; 35; 36]. These angle prediction algorithms included linear regression, quadratic regression, cubic regression, ridge regression, support vector regression, eXtreme gradient boosting, and feed-forward deep neural networks. Our first objective was to explore the use of open-source computer vision algorithms to predict wrist kinematics from a low-cost, off-the-shelf camera system. Our next objective was to explore the use of regression algorithms and machine learning to improve prediction accuracy of the open-source computer vision algorithms. The final objective was to determine if machine learning could increase the angle prediction accuracy to such a degree that a single camera could predict wrist angle within a similar accuracy range as a multiple camera system.