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Digital educational games research tends to lack ecological validity by not adequately taking into account the views and perspectives of children and young people with autism spectrum disorders (ASD). This paper is a pilot study that explores and analyses an academic-based educational game that was co-designed with and for young people with ASD. The serious game aims to help the players learn Geography-specific knowledge and integrates several strategic features so that users can collaborate together against the computer or compete against each other. The educational game was evaluated over 5 sessions by 3 peer teams from 2 different special educational institutions, involving a total of 6 students with ASD. The participants were positive about their enjoyment, motivation, and social engagement. The results showed that the players' level of competitiveness not only influenced the experience within the game but also the interaction within the peer teams. The game mechanisms did help the participants with ASD increase their knowledge in Geography content. The main conclusion is that there are considerable benefits of including children with ASD in the design process and future research should explore more fully on how their involvement can enhance curriculum-based learning as well as social engagement within the classroom.

Background: Acquiring motor skills is fundamental for children's development since it is linked to cognitive development. However, access to early detection of motor development delays is limited., Aim: This review explores the use and potential of motion-based technology (MBT) as a complement to support and increase access to motor screening in developing children., Methods: Six databases were searched following the PRISMA guidelines to search, select, and assess relevant works where MBT recognised the execution of children's motor skills., Results: 164 studies were analysed to understand the type of MBT used, the motor skills detected, the purpose of using MBT and the age group targeted., Conclusions: There is a gap in the literature aiming to integrate MBT in motor skills development screening and assessment processes. Depth sensors are the prevailing technology offering the largest detection range for children from age 2. Nonetheless, the motor skills detected by MBT represent about half of the motor skills usually observed to screen and assess motor development. Overall, research in this field is underexplored. The use of multimodal approaches, combining various motion-based sensors, may support professionals in the health domain and increase access to early detection programmes., Competing Interests: Declaration of Competing Interest The authors have no conflicts of interest to disclose., (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)

Neck disorders have a significant impact on people because of their high incidence. The head-mounted display (HMD) systems, such as Meta Quest 2, grant access to immersive virtual reality (iRV) experiences. This study aims to validate the Meta Quest 2 HMD system as an alternative for screening neck movement in healthy people. The device provides data about the position and orientation of the head and, thus, the neck mobility around the three anatomical axes. The authors develop a VR application that solicits participants to perform six neck movements (rotation, flexion, and lateralization on both sides), which allows the collection of corresponding angles. An InertiaCube3 inertial measurement unit (IMU) is also attached to the HMD to compare the criterion to a standard. The mean absolute error (MAE), the percentage of error (%MAE), and the criterion validity and agreement are calculated. The study shows that the average absolute errors do not exceed 1° (average = 0.48 ± 0.09°). The rotational movement's average %MAE is 1.61 ± 0.82%. The head orientations obtain a correlation between 0.70 and 0.96. The Bland-Altman study reveals good agreement between the HMD and IMU systems. Overall, the study shows that the angles provided by the Meta Quest 2 HMD system are valid to calculate the rotational angles of the neck in each of the three axes. The obtained results demonstrate an acceptable error percentage and a very minimal absolute error when measuring the degrees of neck rotation; therefore, the sensor can be used for screening neck disorders in healthy people.

Objective: Motor and cognitive development share biological background within the prefrontal cortex and cerebellum. Monitoring motor development is relevant to identify children at risk of developmental delays. However, access to timely assessment is prevented by its availability and cost. Affordable motion capture technology may provide an alternative to human assessment., Methods: MotorSense uses this technology to guide and assess children executing age-related developmental motor tasks. It incorporates advanced heuristics informed by pattern recognition principles based on the developmental sequences of motor skills. MotorSense was evaluated with 16 4-6 year-old children from a rural primary school., Results: A total of 506 jumps, 2415 steps and 831 hops were analysed. The analysis illustrates MotorSense Accuracy (MA), recognising jump forward (89.96%), jump high (83.34%), jump sideway (85.63%), hop (74.58%) and jog (92.34%), is as good as the sensor's precision. The analysis of the tasks' execution shows a high level of agreement between human and MotorSense's assessment on jump forward (91%), jump high (99%), jump sideway (93%), hop (94%) and jog (92%)., Conclusions: MotorSense helps address the shortage of affordable technologies to support the assessment of motor development using graded age-related developmental motor tasks. Furthermore, it could contribute towards the tele-detection of motor developmental delays., Competing Interests: The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© The Author(s) 2022.)

Subject calibration has been demonstrated to improve the accuracy in high-performance eye trackers. However, the true weight of calibration in off-the-shelf eye tracking solutions is still not addressed. In this work, a theoretical framework to measure the effects of calibration in deep learning-based gaze estimation is proposed for low-resolution systems. To this end, features extracted from the synthetic U2Eyes dataset are used in a fully connected network in order to isolate the effect of specific user's features, such as kappa angles. Then, the impact of system calibration in a real setup employing I2Head dataset images is studied. The obtained results show accuracy improvements over 50%, probing that calibration is a key process also in low-resolution gaze estimation scenarios. Furthermore, we show that after calibration accuracy values close to those obtained by high-resolution systems, in the range of 0.7°, could be theoretically obtained if a careful selection of image features was performed, demonstrating significant room for improvement for off-the-shelf eye tracking systems.

MotorSense is a motion detection and tracking technology that can be implemented across a range of environments to assist in detecting delays in gross-motor skills development. The system utilises the motion tracking functionality of Microsoft's Kinect™. It features games that require children to perform graded gross-motor tasks matched with their chronological and developmental ages. This paper describes the rationale for MotorSense, provides an overview of the functionality of the system and illustrates sample activities.