Motor learning and transfer between real and virtual environments in young people with autism spectrum disorder: a prospective randomized cross over controlled trial

Autism spectrum disorder (ASD) is associated with persistent deficits in social communication and social interaction, including impaired multisensory integration which might negatively impact cognitive and motor skill performance, and hence negatively affect learning of tasks. Considering that tasks in virtual environment may provide an engaging tool as adjuncts to conventional therapies, we set out to compare motor performance between young people with ASD and a typically developing (TD) control group that underwent coincident timing tasks based on Kinect (no physical contact) and on Keyboard (with physical contact) environments. Using a randomized repeated cross-over controlled trial design, fifty young people with ASD and fifty with TD, matched by age and sex were divided into subgroups of 25 people that performed the two first phases of the study (acquisition and retention) on the same device – real or virtual – and then switched to the other device to repeat acquisition and retention phases and finally switched on to a touch screen (transfer phase). Results showed that practice in the virtual task was more difficult (producing more errors), but led to a better performance in the subsequent practice in the real task, with more pronounced improvement in the ASD as compared to the TD group. It can be concluded that the ASD group managed to transfer the practice from a virtual to a real environment, indicating that virtual methods may enhance learning of motor and cognitive skills. A need for further exploration of its effect across a number of tasks and activities is warranted.

Motion-based technology to support motor skills screening in developing children: A scoping review

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.Funding for open access charge: Universidad de Málaga / CBUA

Virtual and augmented reality in social skills interventions for individuals with autism spectrum disorder: A scoping review

In the last decade, there has been an increase in publications on technology-based interventions for autism spectrum disorder (ASD). Virtual reality based assessments and intervention tools are promising and have shown to be acceptable amongst individuals with ASD. This scoping review reports on 49 studies utilizing virtual reality and augmented reality technology in social skills interventions for individuals with ASD. The included studies mostly targeted children and adolescents, but few targeted very young children or adults. Our findings show that the mode number of participants with ASD is low, and that female participants are underrepresented. Our review suggests that there is need for studies that apply virtual and augmented realty with more rigorous designs involving established and evidenced-based intervention strategies.publishedVersio

Effect of sensory-based technologies on atypical sensory responses of children with Autism Spectrum Disorder: A systematic review

© 2021 ACM, Inc. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1145/3485768.3485782.Atypical sensory responses are one of the most common issues observed in Autism Spectrum Disorder (ASD), affecting the development of a child's capability for social interaction, independent living and learning. In the past two decades, there has been a growing number of studies of technology-based interventions for atypical sensory responses of individuals with ASD. However, their effects and limitations have not been fully examined. This systematic review investigates the effects of sensory-based technologies (SBTs) on atypical sensory responses of children with ASD. Publications that report on the use of a SBT as an intervention tool were retrieved from four academic databases: “PubMed”, “IEEE Xplore”, “ACM Digital Library” and “Web of Science”. The search finally yielded 18 articles. The results indicated an emerging trend of studies investigating the effects of SBTs on atypical sensory responses over the past decade. Challenges and limitations were found in studies, mainly because the literatures adopted different methods and indicators, small sample sizes, and varying experimental designs. Findings were that the use of SBTs could effectively improve auditory and visual recognition, and some other behavioural outcomes such as attention in children with ASD. Future development of SBTs could further integrate more advanced techniques, such as machine learning, in order to widen the scope of SBTs usage to help more ASD children

Children with Motor Impairments Play a Kinect Learning Game: First Findings from a Pilot Case in an Authentic Classroom Environment

This paper presents the first very positive findings from an empirical study about the effectiveness of the use of a Kinect learning game for children with gross motor skills problems and motor impairments. This game follows the principles of a newly presented approach, called Kinems, which advocates that special educators and therapists should use learning games that via embodied touchless interaction – thanks to the Microsoft Kinect camera- children with dyspraxia and other related disorders such as autism, Asperger's Syndrome, and Attention Deficit Disorder, can improve related skills. Several Kinems games have been proposed (http://www.kinems.com). These games are innovative and are played with hand and body gestures. Kinems suggests that games should be highly configurable so that a teacher can modify the settings (e.g. difficult level, time settings, etc.) for the individual needs of each child. Also, a teacher should have access to kinetic and learning analytics of the child’s interaction progress and achievements should be safely stored and vividly presented

Student proposals for design projects to aid children with severe disabilities

Citation: Warren, S. (2016). Student proposals for design projects to aid children with severe disabilities.Children with severe disabilities have unique individual needs. Technology-based designs intended to quantify the well-being of these children or assist them with learning or activities of daily living are often by nature "one of" designs tightly matched to these needs. For children with severe autism, such designs must be incorporated into their environments in unobtrusive ways to avoid upsetting or distracting these children. This design space and its affiliated challenges offer a rich environment for engineering students to exercise their design creativity. This paper presents an end-of-semester exercise for a Kansas State University Introduction to Biomedical Engineering class, where students propose senior-design projects geared toward children with severe disabilities. The goal of the exercise is to integrate concepts related to biomedical devices, design factors, care delivery environments, and assistive technology into a proposed design with clear practical benefit that can be implemented in prototype form by a senior design team over the span of about two semesters. The deliverable for the design exercise is a four-page paper in two-column IEEE format that adheres to a pre-specified structure. To focus these design-project ideas, students are asked to offer their thoughts within the framework of needs specified by clinical staff at Heartspring in Wichita, KS, a facility that serves severely disabled children, where nearly all of the full-time residents are autistic, and most are nonverbal. In addition to the educational benefits offered by this experience, the author's intent is to help spur ideas for new senior design projects that can be supported with resources from existing NSF-funded grants which provide equipment and materials for such endeavors. Six semesters worth of design ideas are presented here, along with the results of assessment rubrics applied to the final papers. The class is populated by students from various departments within the Kansas State University College of Engineering, so design proposals are varied and incorporate low-level to system-level solutions. Some of these design ideas have been adopted by design teams, whereas others await attention. © American Society for Engineering Education, 2016

Automatic detection of ADHD and ASD from expressive behaviour in RGBD data

Attention Deficit Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD) are neurodevelopmental conditions which impact on a significant number of children and adults. Currently, the diagnosis of such disorders is done by experts who employ standard questionnaires and look for certain behavioural markers through manual observation. Such methods for their diagnosis are not only subjective, difficult to repeat, and costly but also extremely time consuming. In this work, we present a novel methodology to aid diagnostic predictions about the presence/absence of ADHD and ASD by automatic visual analysis of a person's behaviour. To do so, we conduct the questionnaires in a computer-mediated way while recording participants with modern RGBD (Colour+Depth) sensors. In contrast to previous automatic approaches which have focussed only on detecting certain behavioural markers, our approach provides a fully automatic end-to-end system to directly predict ADHD and ASD in adults. Using state of the art facial expression analysis based on Dynamic Deep Learning and 3D analysis of behaviour, we attain classification rates of 96% for Controls vs Condition (ADHD/ASD) groups and 94% for Comorbid (ADHD+ASD) vs ASD only group. We show that our system is a potentially useful time saving contribution to the clinical diagnosis of ADHD and ASD

Exploring the impact of augmented reality in children and adolescents with Autism Spectrum Disorder: a systematic review

Autistic Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by persistent difficulties in communication and social interaction along with a restriction in interests and the presence of repetitive behaviors. The development and use of augmented reality technology for autism has increased in recent years. However, little is known about the impact of these virtual reality technologies on clinical health symptoms. The aim of this systematic review was to investigate the impact of augmented reality through social, cognitive, and behavioral domains in children and adolescents with autism. This study is the first contribution that has carried out an evidence-based systematic review including relevant science databases about the effectiveness of augmented reality-based intervention in ASD. The initial search identified a total of 387 records. After the exclusion of papers that are not research studies and are duplicated articles and after screening the abstract and full text, 20 articles were selected for analysis. The studies examined suggest promising findings about the effectiveness of augmented reality-based treatments for the promotion, support, and protection of health and wellbeing in children and adolescents with autism. Finally, possible directions for future work are discussed.Psicologí

Motion and emotion estimation for robotic autism intervention.

Robots have recently emerged as a novel approach to treating autism spectrum disorder (ASD). A robot can be programmed to interact with children with ASD in order to reinforce positive social skills in a non-threatening environment. In prior work, robots were employed in interaction sessions with ASD children, but their sensory and learning abilities were limited, while a human therapist was heavily involved in “puppeteering” the robot. The objective of this work is to create the next-generation autism robot that includes several new interactive and decision-making capabilities that are not found in prior technology. Two of the main features that this robot would need to have is the ability to quantitatively estimate the patient’s motion performance and to correctly classify their emotions. This would allow for the potential diagnosis of autism and the ability to help autistic patients practice their skills. Therefore, in this thesis, we engineered components for a human-robot interaction system and confirmed them in experiments with the robots Baxter and Zeno, the sensors Empatica E4 and Kinect, and, finally, the open-source pose estimation software OpenPose. The Empatica E4 wristband is a wearable device that collects physiological measurements in real time from a test subject. Measurements were collected from ASD patients during human-robot interaction activities. Using this data and labels of attentiveness from a trained coder, a classifier was developed that provides a prediction of the patient’s level of engagement. The classifier outputs this prediction to a robot or supervising adult, allowing for decisions during intervention activities to keep the attention of the patient with autism. The CMU Perceptual Computing Lab’s OpenPose software package enables body, face, and hand tracking using an RGB camera (e.g., web camera) or an RGB-D camera (e.g., Microsoft Kinect). Integrating OpenPose with a robot allows the robot to collect information on user motion intent and perform motion imitation. In this work, we developed such a teleoperation interface with the Baxter robot. Finally, a novel algorithm, called Segment-based Online Dynamic Time Warping (SoDTW), and metric are proposed to help in the diagnosis of ASD. Social Robot Zeno, a childlike robot developed by Hanson Robotics, was used to test this algorithm and metric. Using the proposed algorithm, it is possible to classify a subject’s motion into different speeds or to use the resulting SoDTW score to evaluate the subject’s abilities