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Commonly used pose estimation algorithms introduce mistakes in kinematic estimation pipelines due to systematic errors in their predictions. To detect key points, most pose estimation methods are trained on a combination of images of a person and ground truth annotations which map pixels in the image to their corresponding joint center.These ground truth annotations are often manually conducted by non-expert annotators, leading to errors caused by personal biases for training and inaccuracies in the pose estimations [9]. For example, Needham et al. [11] compared three often used pose estimation algorithms OpenPose [12], DeepLabCut [13] and AlphaPose [14] algorithm against an OMC system and showed errors in the estimation of joint centers of 30 mm to 50 mm with variations in 12 mm to 25 mm in marker placement. Cronin [9] provides an overview of additional problems with 2D pose estimation for kinematic analysis. These differences are most likely due to a difference between the application that pose estimation algorithms are often developed for and their application to, e.g., the biomedical domain, which has different accuracy requirements [8]. Wade et al. [10] proposed to solve this problem by re-annotating existing large-scale datasets, this, however, is a time-consuming process, when for example considering the COCO-keypoint dataset -2020 (accessed on 2 December 2022) consists of more than 250.000 labeled poses. For the evaluation of pose estimation algorithms, these labeling errors will just appear as a baseline error that all algorithms training on the same data will have. However, for applications in the biomedical domain and in situations such as kinematic estimation, where the pose is just an intermediate step errors can propagate to subsequent tasks.
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The capabilities of D3KE as an adapter for kinetic analysis of a movement in OpenSim could be explored. Given data similar to BMLMovi or successful transfer learning on relevant data beforehand, our method provides an easy way to skip the tedious steps of scaling and running inverse kinematics on an MSM. This enables the quick generation of MSMs for kinetic analysis from just a single video. Even if this kinematic estimation comes at the cost of reduced accuracy, it could provide coarse insights into collected data, which can later be confirmed through finer analysis with the manually scaled MSMs. 2ff7e9595c
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