## 1 Introduction  
Motor behavior is regulated by the sensorimotor control system. This system integrates sensory input (i.e., visual, vestibular and somatosensory inputs) to execute motor commands, resulting in muscular responses [1]. Proprioception is a subsystem of the somatosensory system. Proprioception is the perception of joint and body movement as well as the position of the body or body segments in space [2]. For example, proprioception allows someone's body segment to be localized without having to rely on visual input alone. Muscle spindles, Golgi tendon organs and other mechanoreceptors located in the skin and capsuloligamentous structures provide proprioceptive information to the central nervous system for central processing [3]. As postural control is also regulated by the sensorimotor control system, the development of postural control in children is related to the maturation of proprioception, more specifically lower limb proprioception. Also, in other populations, difficulties in processing, integrating and (consciously) perceiving proprioceptive input can lead to impaired motor performance [4; 5; 6; 7; 8; 9; 10; 11; 12; 13]. Training programs have been shown to lead to improved proprioceptive function in various populations [14]. As such, it is important to accurately measure proprioceptive function to identify potential deficits (as soon as possible). Different senses of proprioception can be measured, such as the ability to perceive joint position, movement (extent, trajectory, and velocity), the level of force, muscle tension and weight [15]. The most commonly used and clinically applicable method to assess proprioception in the context of motor behavior is the Joint Position Reproduction (JPR) test, which assesses the sense of joint position [15; 16; 17]. The JPR test examines the ability to actively or passively reproduce a previously presented target joint position [18]. The examiner-positioned joint angle is compared with the joint angle repositioned by the subject. Differences between the targeted and reproduced joint angles indicate proprioceptive accuracy (i.e., a measure of proprioceptive function) and are defined as angular errors (i.e., joint position reproduction error (JRE)). The JRE could be expressed as an absolute error, which considers the absolute magnitude of the error (regardless of the direction of the error) and a systematic error, which considers the magnitude and direction of the error. The gold standard to accurately measure reliable joint angles is laboratory-based optoelectronic three-dimensional motion capture. Unfortunately, the need to attach markers and the need for a high-tech laboratory inhibit the routine collection of valuable high-quality data, because it is very expensive and time-consuming. In addition, incorrect and inconsistent marker placement and soft tissue artifacts are common, and minimal or tight-fitting clothing is preferred to avoid these [19; 20]. Recent advances in biomedical engineering resulted in new techniques based on deep learning to track body landmarks in simple video recordings, which include a high degree of automatization, and allow recordings in an unobtrusive manner in a natural environment. The manually placed, skin-mounted markers could be replaced with automatically detected landmarks with these deep-learning-based techniques. In humans, the development and application of systems that can estimate two- and three-dimensional human poses by tracking body landmarks without markers under a variety of conditions are increasing (e.g., Theia3D, DeepLabCut, Captury, OpenPose, Microsoft Kinect, Simi Shape). DeepLabCut is a free open-source toolbox that builds on a state-of-the-art pose-estimation algorithm to allow a user to train a deep neural network to track user-defined body landmarks [21; 22]. DeepLabCut distinguishes itself because it is free and it is possible to use your own model or extend and retrain a pre-trained model in which joints are annotated.