## 2 Background  
Eye-hand coordination (EHC) is a complicated process that requires the precise activation of ocular and manual motor systems [29]. Typically, gold standard clinical tests include EHC assessment sections involving tasks such as the placement of objects in holes, reaching, and grasping small objects. The rating of motor coordination is assessed through spatial and/or temporal variables [30] (e.g., completion time, smoothness, and other performance-based indicators). However, these methods may be affected by subjective factors such as inter-operator variability (experimenter bias) and the use of gross hand/arm movements, which require not only EHC but also arm control. Therefore, there are several technological systems available that address the assessment of EHC and motor skills. One early method for EHC assessment was the 'Moore Eye-Hand Coordination and Colour-Matching Test', which measured the time taken to place marbles in holes and match coloured marbles with colour-coded holes as quickly as possible [31]. A similar modern assessment method is that proposed by [PERSON] et al. [25], which involves an instrumented version of the NHPT. This system involves manipulating an instrumented handle to place nine pegs into nine holes displayed in a 2D virtual environment. Movement coordination is measured by the number of zero-crossings in the hand acceleration vector. Similarly, a vision-based approach was proposed by [PERSON] et al. [32] to automatically score hand coordination based on the NHPT. This system extracts multiple kinematic parameters (period, velocity, time) to objectively rate motor coordination using machine learning techniques. Other systems have been developed that use computer-based interaction to estimate EHC. For example, in a previous work by the authors of the present paper, the use of a tablet pen system was proposed to capture hand trajectories in drawing-like tasks [28]. However, this system was not evaluated. [PERSON] et al. developed and tested a tablet-based system that involves tracing up to 13 shapes with a stylus pen [7]. Each shape corresponds to a different difficulty level based on the complexity and length of the outline. This system can automatically store the time taken to complete each trace and the spatial accuracy of the tracing. Their results suggest that this method is suitable for the assessment of visuomotor capacity in patients after stroke [33]. Despite the above-mentioned systemsreducing subjectivity in the evaluation, this kind of system is also limited to fine motor skills assessment due to the small size of the NHPT device or the tablet screen size. In this sense, other methods have tried to increase the workspace range to allow for measuring gross motor coordination by promoting performing tasks in larger workspaces. [PERSON] et al. [34] presented a camera-based system that gathers information on the player's motor performance with respect to the visual stimuli presented in a game. The system uses two cameras to estimate the arm pose, and based on its reaching response to random visual stimuli, eye-hand coordination is assessed. Similarly, [PERSON] et al. [35] designed a sensor-based system to assess eye-hand coordination in tasks involving reaching. The system consists of a steel frame, a series of touch sensors, an LCD, and a carpet for the testee to stand on. EHC is evaluated based on the execution time and the number of sensors the user has successfully turned off by touching. It should be noted that reaching tasks demand higher interjoint coordination than writing because they involve larger muscle masses [36; 37]. Despite most of the previous solutions utilising game-like interfaces, a different approach is the use of fully VR-based systems to develop virtual versions of the gold standard clinical tests. For example, the virtual BBT [22; 24], which is used to measure hand coordination when grasping and moving small virtual cubes, or virtualised versions of the NHPT [38; 39], where small virtual pegs must be grasped and arranged.