Whole-Body Movement during Videogame Play Distinguishes Youth with Autism from Youth with Typical Development (original) (raw)
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Scientific Reports, 2016
Autism is a developmental disorder evident from infancy. Yet, its clinical identification requires expert diagnostic training. New evidence indicates disruption to motor timing and integration may underpin the disorder, providing a potential new computational marker for its early identification. In this study, we employed smart tablet computers with touch-sensitive screens and embedded inertial movement sensors to record the movement kinematics and gesture forces made by 37 children 3–6 years old with autism and 45 age-and gender-matched children developing typically. Machine learning analysis of the children's motor patterns identified autism with up to 93% accuracy. Analysis revealed these patterns consisted of greater forces at contact and with a different distribution of forces within a gesture, and gesture kinematics were faster and larger, with more distal use of space. These data support the notion disruption to movement is core feature of autism, and demonstrate autism can be computationally assessed by fun, smart device gameplay. Autism spectrum disorder (ASD) is a childhood neurodevelopmental disorder. Its global prevalence is estimated at 1 in 160 children 1. The European and North American prevalence of autism is estimated to be 1 in 68 children 2. In the UK, ca. 700,000 individuals live with autism 3 and the aggregate cost of healthcare and support is £27.5 billion annually 4. The cause of ASD is not well understood and its aetiology is complex, involving both genetic and environmental factors 5,6. It is generally recognised the most effective clinical route to treatment is its early identification and consequent early therapeutic intervention 7,8. Early diagnosis can also afford the family and caregivers opportunity to adjust, and in some cases can trigger the resources required for professional care and treatment. Such provision can produce significant health and economic benefit, offering the best chance for lifelong improvement and relative independence 9,10. Yet, although early diagnosis and intervention appears to offer the best chance for significant health improvement and economic gain, diagnosis of autism remains complex and often difficult to obtain. It currently relies on specialist medical expertise with diagnostic instrumentation that depends on interpretative coding of child observations , parent interviews, and manual testing. These instruments are time consuming and clinically demanding. Medical diagnosis can be withheld for many years due to wait-list times or uncertainty in clinical diagnostic fit. Recent identification of motor disturbance in young children who develop ASD presents a new target for the development of early assessment methodologies 11. ASD is typically considered a social and emotional disorder. Therefore, current diagnostic instruments directly address social and emotional aspects of the syndrome. However, motor control underpins social engagement, emotional expression, and cognitive development 12–17 , and children with ASD exhibit a clear deficit in movement observable from birth 18 and evident throughout life 19–25. This motor perspective on autism is beginning to gain some significant clinical and research interest 11,26,27. Disruption of normal movement patterns first identified by Kanner 28 is a cardinal feature of ASD and is becoming increasingly recognised as a likely primary deficit in ASD aetiology 11. Repetitive movements and restricted interests are core diagnostic criteria in professional clinical practice in both the United States
PloS one, 2024
In recent years, research has been demonstrating that movement analysis, utilizing machine learning methods, can be a promising aid for clinicians in supporting autism diagnostic process. Within this field of research, we aim to explore new models and delve into the detailed observation of certain features that previous literature has identified as prominent in the classification process. Our study employs a game-based tablet application to collect motor data. We use artificial neural networks to analyze raw trajectories in a "drag and drop" task. We compare a two-features model (utilizing only raw coordinates) with a fourfeatures model (including velocities and accelerations). The aim is to assess the effectiveness of raw data analysis and determine the impact of acceleration on autism classification. Our results revealed that both models demonstrate promising accuracy in classifying motor trajectories. The four-features model consistently outperforms the two-features model, as evidenced by accuracy values (0.90 vs. 0.76). However, our findings support the potential of raw data analysis in objectively assessing motor behaviors related to autism. While the fourfeatures model excels, the two-features model still achieves reasonable accuracy. Addressing limitations related to sample size and noise is essential for future research. Our study emphasizes the importance of integrating intelligent solutions to enhance and assist autism traditional diagnostic process and intervention, paving the way for more effective tools in assessing motor skills.
Using Technology to Identify Children With Autism Through Motor Abnormalities
Frontiers in Psychology, 2021
Autism is a neurodevelopmental disorder typically assessed and diagnosed through observational analysis of behavior. Assessment exclusively based on behavioral observation sessions requires a lot of time for the diagnosis. In recent years, there is a growing need to make assessment processes more motivating and capable to provide objective measures of the disorder. New evidence showed that motor abnormalities may underpin the disorder and provide a computational marker to enhance assessment and diagnostic processes. Thus, a measure of motor patterns could provide a means to assess young children with autism and a new starting point for rehabilitation treatments. In this study, we propose to use a software tool that through a smart tablet device and touch screen sensor technologies could be able to capture detailed information about children’s motor patterns. We compared movement trajectories of autistic children and typically developing children, with the aim to identify autism moto...
2014
Evans, J.P. Physical activity level comparisons of individuals with autism spectrum disorders while playing active video games. MS in Exercise and Sport Science-Physical Education Teaching, Adapted Physical Education Emphasis, August 2014, 76pp. (E. Felix) Many students with Autism Spectrnm Disorders (ASD) receive adapted or specially designed physical education. Active video games (A VGs) are tools adapted physical education (APE) teachers use to promote physical activity (PA) in students with disabilities. However, little research exists on what game consoles are effective. The purpose of this study was to compare XBOX Kinect and Nintendo Wii active video game (A VG) consoles to see if either console elicited more physical activity (PA). Participants (N=15) included 12-21 year old youth with ASD who were tested in a counter balanced order once on XBOX Kinect and once on Nintendo Wii while playing Boxing. Each testing session started with a collection of anthropometric measures. Tw...
Brain Disorders, 2022
The motor system is becoming increasingly recognized as an important site of disruption in autism spectrum disorders (ASD). However, the precise nature of this motor disruption remains unclear with some conflicting reports. We employed a smart tablet serious game approach, which did not require verbal instruction. Children's movements on the touch screen were recorded, and their kinematics computed from two games. One afforded goal-directed swipes, and the other free-style colouring. Children aged 25-79 months participated in this study, including 37 children with ASD and 45 typically developing (TD) children. Results revealed significant group, age, and task differences. In comparison to controls, children with ASD <5 years old performed faster goaldirected swipes, whereas those ≥5 years old performed slower goal-directed swipes. In contrast, during freestyle drawing, children with ASD moved faster than the controls irrespective of age. Within the TD participants, the older subgroup (≥5 years) performed faster movements than the younger subgroup (<5 years) in both game contexts. However, the ASD older subgroup moved slower than their younger subgroup in the case of goaldirected swipes while no speed difference was observed in the case of free-style drawing. These findings reveal developmental differences in motor development in young children with ASD from their TD counterparts. Further, they demonstrate smart tablet gameplay can produce precise computational metrics of motor kinematics to characterize these differences deployable in schools, clinics and home settings for large-scale data collection for both research and clinical purposes that may ultimately enable accessible and scalable early detection of ASD.
Motor signature of autism spectrum disorder in adults without intellectual impairment
Scientific Reports
Motor signs such as dyspraxia and abnormal gait are characteristic features of autism spectrum disorder (ASD). However, motor behavior in adults with ASD has scarcely been quantitatively characterized. In this pilot study, we aim to quantitatively examine motor signature of adults with ASD without intellectual impairment using marker-less visual-perceptive motion capture. 82 individuals (37 ASD and 45 healthy controls, HC) with an IQ > 85 and aged 18 to 65 years performed nine movement tasks and were filmed by a 3D-infrared camera. Anatomical models were quantified via custom-made software and resulting kinematic parameters were compared between individuals with ASD and HCs. Furthermore, the association between specific motor behaviour and severity of autistic symptoms (Autism Diagnostic Observation Schedule 2, Autism Spectrum Quotient) was explored. Adults with ASD showed a greater mediolateral deviation while walking, greater sway during normal, tandem and single leg stance, a ...
Journal of NeuroEngineering and Rehabilitation, 2022
Background Motor impairment is widely acknowledged as a core feature in children with autism spectrum disorder (ASD), which can affect adaptive behavior and increase severity of symptoms. Low-cost motion capture and virtual reality (VR) game technologies hold a great deal of promise for providing personalized approaches to motor intervention in ASD. The present study explored the feasibility, acceptability and potential efficacy of a custom-designed VR game-based intervention (GaitWayXR™) for improving gross motor skills in youth with ASD. Methods Ten children and adolescents (10–17 years) completed six, 20-min VR-based motor training sessions over 2 weeks while whole-body movement was tracked with a low-cost motion capture system. We developed a methodology for using motion tracking data to quantify whole-body movement in terms of efficiency, synchrony and symmetry. We then studied the relationships of the above quantities with standardized measures of motor skill and cognitive fle...
Frontiers in Psychology
Many symptoms of the autism spectrum disorder (ASD) are evident in early infancy, but ASD is usually diagnosed much later by procedures lacking objective measurements. It is necessary to anticipate the identification of ASD by improving the objectivity of the procedure and the use of ecological settings. In this context, atypical motor skills are reaching consensus as a promising ASD biomarker, regardless of the level of symptom severity. This study aimed to assess differences in the whole-body motor skills between 20 children with ASD and 20 children with typical development during the execution of three tasks resembling regular activities presented in virtual reality. The virtual tasks asked to perform precise and goal-directed actions with different limbs vary in their degree of freedom of movement. Parametric and non-parametric statistical methods were applied to analyze differences in children’s motor skills. The findings endorsed the hypothesis that when particular goal-direct...
It has been hypothesized that individuals with Autism Spectrum Disorder (hereafter ‘autism’) have problems perceiving biological motion, which contributes to their social difficulties. However, the ability to perceive the kinematic profile characteristic of biological motion has not been systematically examined in autism. To examine this basic perceptual ability we conducted two experiments comparing adults with autism with matched typical adults. In Experiment 1, participants indicated whether two movements – which differed in the quantity of formula-generated biological motion – were the same or different. In Experiment 2, they judged which of two movements was ‘less natural’, where the stimuli varied in the degree to which they were a product of real movement data produced by autistic and typical models. There were no group differences in perceptual sensitivity in either experiment, with null effects supported by Bayesian analyses. The findings from these two experiments demonstrate that adults with autism are sensitive to the kinematic information defining biological motion to a typical degree – they are both able to detect the perceptual information in a same-different judgment, and as inclined to categorize biological motion derived from real models as natural. These findings therefore provide evidence against the hypothesis that individuals with autism exhibit low-level difficulties in perceiving the kinematics of others’ actions, suggesting that atypicalities arise either when integrating this kinematic information with other perceptual input, or in the interpretation of kinematic information.