## 2 Materials and Methods
### Data Collection  
To recruit for the sample, many calls were made through health and educational sectors that support children with NDs and no-NDs. For each child participant, an adult has also been involved to provide the required (parental) consent and the child's developmental and communication history. The project's nature, purpose, procedures, and approval by the University of Ioannina Research Ethics Committee, Greece (Reg. Num. : 18435/15 May 2020), which complies with the General Data Protection Regulation GDPR, were then thoroughly explained to parents during an informative meeting. Parents then endorsed the consent form. Next, the child interacts with the serious game, under the clinician's guidance. The game is designed to record the child's responses while playing the game along with biometric measurements, i.e., eye tracking and heart rate. The responses are quantified as variables forming four categories, more specifically, hand movements on the touch screen, verbally answering questions or executing commands, eye tracking data while watching scenes, and spontaneous heart rate reactions in real time. Regarding the first category, the game automatically outputs the scores that correspond to the player's performance. The remaining variables are analyzed as follows. The digital game employs procedures to recognize words through the built-in speech-to-text (STT) capability. The participant is asked in several phases of the game to pronounce words such as characters' names and objects of the game. The words that make up the correct answers are predetermined. The duration of each recording is 10 s, long enough to capture the participant's response. For the word recognition, the speech-to-text program CMUSphinx [72] has been chosen. It is available for free, it can work on different operating systems (desktop, mobile), it is relatively fast, and it works offline. In this software, a corresponding recognition model in Greek has been created and trained [73]. There are a total of 40 words that are expected to be \"heard\" in the targeted language, that is, Greek. The program essentially detects which word from the above best matches what the child has said and consequently whether the child gave a correct answer or not. During the game and in real time the player wears a smartwatch which records the heart rate. The values are sent online to the database where they are synchronized with the different phases of the game. Depending on the activity we are interested in samples collected from the specific time period and the corresponding statistics variables are calculated. Mean, standard deviation, and range for every distinct activity of the game are the heart rate (HR) variables. HRV is the variation in the time difference between successive heart beats, and several ways of calculation have been defined [33]. For the exact calculation one should have the information of the time difference of successive pulses, and the most reliable way is with the electrocardiogram (ECG). The wearable device (smartwatch) used in the game allows only the heart rate to be measured, not the individual pulses. From the heart rate (HR) it is not possible to calculate HRV directly, especially when filtering-smoothing techniques are used by the measuring devices which alter the original information of the measurements. However, to obtain an estimate of the rate variability, since it is considered a more important feature than the rate itself, we have calculated the heart rate standard deviation and range statistics as an alternative. Unity environment (Unity(r), 2022) is used to implement the game. Eye tracking is provided through the SeeSo software ([PERSON] et al., 2017). This is achieved by detecting the position of the eyes through the camera of the mobile device (tablet) and allows the calculation of the target the observer is looking at on the screen.