## 1 Introduction
[21] automated the Fugl-Meyer Upper-Extremity (FMUE) test using a virtual reality-based platform to evaluate motor coordination via endpoint trajectories. The results were highly correlated with the standard FMUE. Virtual versions of the BBT are also available using sensors such as the Microsoft Kinect V1 [22] or the Leap Motion Controller (LMC) [23; 24] to detect the hand and its movements during virtual cube transfers. [PERSON] et al. [25] presented a virtual version of the NHPT that involves manipulating an instrumented handle in order to move nine pegs into nine holes displayed in a virtual environment. Movement coordination is measured by the number of zero-crossings of the hand acceleration vector. Overall, these virtual versions of motor coordination assessment methods present a high correlation with the real systems, showing a promising use of this technology for upper-extremity and eye-hand coordination applications. Different sensors and technologies have been used to measure motor coordination, allowing the capture of different parameters related to motor performance (position, velocity, pressure, etc.). This highlights the importance of making multiple devices available to clinicians to extract as many parameters as possible during evaluation. Thus, multimodality can enhance the effectiveness of serious games through multimodal sensory integration (devices to capture human factors) [26]. Traditional input/output devices include the mouse, keyboard, and touchpad. There are also advanced devices such as eye trackers, hand trackers, haptics, motion trackers, gesture controllers, and EEG and EMG devicesto name a few [27]. Proper multimodal integration in serious games is still challenging in neurological rehabilitation since a one-size-fits-all system cannot address all patients' needs. On this basis, this paper presents a system to measure eye-hand coordination ability using a virtual scenario and multimodal devices. In a previous work [28], the development of the virtual environment was briefly presented, and the main goals of the proposed framework were established. Also, two input devices with small workspaces, such as the mouse and tablet pen, were included (related to fine motor skills). The present article extends the description of the virtual environment, proposing generalisable tools to develop similar serious games. Additionally, the main novelty of this paper is the integration of a robotic arm as an input device, which promotes a higher demand for interjoint coordination when performing the drawing task due to the larger workspace (related to gross motor skills). To the best of our knowledge, there is no game-based system available that addresses the assessment of both fine and gross motor skills. Additionally, a case study with healthy participants was conducted to analyse the feasibility of multimodal systems for capturing hand trajectories and the viability of using these data to compare the user's performance when executing drawing-like tasks with input devices that demand different interjoint coordination. The remainder of this article is organised as follows. Section 2 presents a summary of related works. In Section 3, the system architecture is presented. Section 4 describes the design process and the main features of the multimodal game-based tool. Section 5 presents the eye-hand coordination game and the integration of multimodal devices, particularly robotic ones. Subsequently, the results of the preliminary trial are presented in Section 6. Finally, the discussion and conclusions can be found in Section 7.