## 1 Introduction  
Playing a pivotal role in many activities of daily living, the wrist is at risk of developing repetitive strain injuries (RSIs) when exposed to repetitive, awkward postures [1, 2, 3]. Work-related musculoskeletal disorders (WMSDs), including RSIs, increase costs, reduce productivity, and impact employee quality of life [3]. Musculoskeletal diseases as a whole are estimated to cost the Canadian economy approximately CAD 22 billion per year [4], with work-related activities associated with over 55% of RSIs and 23% of total RSIs affecting the wrist or hand [5]. To decrease the economic burden, methods of monitoring joint kinematics and therefore risk,in situ, are needed. The measurement of wrist kinematics during the performance of work activities allows ergonomists and other professionals to gain insight into the etiology of RSI development, as well as the efficacy of risk reduction interventions [6]. In controlled laboratory settings, upper extremity kinematics are often quantified using optical motion capture (OMC) systems [7; 8; 9; 10; 11]. The use of OMC technology outside the lab is difficult due to challenges such as high cost, arduous preliminary setup and trained personnel requirements, environmental and spatial limitations, and long data processing times [12; 13; 14; 15]. With developments in computer vision and machine learning (ML), more automated methods involving markerless motion capture (MMC) technology can now be used to track joint kinematics [16; 17]. Quantification of kinematics in the workplace and clinical settings is more amenable to MMC since it does not impede the subject with marker application [12]. MMC reduces the intrusive nature of marker-based motion capture; however, it has limited accuracy when applied to the wrist [18]. Additionally, commercially available markerless systems are costly [19]. Minimizing the number and cost of the cameras coupled with further automation of risk classification may help to improve MMC accessibility for workplace applications. These improvements in accessibility would move the current state of the art to an ideal solution where a single camera is used to monitor joint kinematics. Recently, open-source machine-learning algorithms have emerged to make MMC more accessible in terms of cost and setup. Examples of open-source MMC methods involving computer vision (CV) algorithms include Alphapose [20], OpenPose [21], MediaPipe [22], DeepLabCut (DLC) [23], and Anipose [24]. Alphapose adapts an open-source human detector to locate a participant and then uses a residual neural network (ResNet) [25] to estimate the pose [20]. OpenPose analyzes images using convolutional neural networks to define confidence maps for locating body parts in an image and encodes the degree of association between body parts. The confidence maps and body part encodings are then parsed to generate 2D key points for all people in the images [21]. MediaPipe is a cross-platform framework that uses a body detector and a pose tracker to detect 33 distinct human body locations for pose estimation [22; 26], whereas DeepLabCut is an open-source software toolbox (DeepLabCut Version 2.3.3, Mathis Lab ([PERSON] & [PERSON]), Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland). 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) was used for pose estimation that makes use of the feature detector architecture from DeeperCut [27; 28]. Human-annotated images are used to train DeepLabCut to 'learn' the feature detectors associated with the annotated body parts, and then the network outputs annotated images when given new unseen inputs [23]. Anipose builds upon the DeepLabCut model to quantify 3D kinematics by combining marker tracking from multiple camera views.