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The use of augmented reality in the diagnosis and treatment of autistic children: a review and a new system

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Abstract

This paper consists of two parts. The first presents a review of the literature on the use of augmented reality (AR) in the diagnosis and treatment of autistic children with a particular focus on the efficacy of AR in assisting autistic children who have communicative, social, sentiment, and attention deficit disorders. The review also investigated interactions between AR systems and children, taking into consideration the target behaviors that are selected from the child during treatment. Such modes were fully explored by taking into account the needs of the individual child in terms of achieving an improvement in their condition. Most significantly, the empirical information that was obtained from the reviewed works was evaluated according to some specific targeted attitudes and how each AR solution was utilized during treatment to achieve the fostering of such attitudes in order to identify the requirements for building an effective AR system. In addition, the review revealed the essential design features that can enable AR systems to achieve a high level of effectiveness in autism therapy. The review also covered the instruction that AR systems were noticed to execute, and focused on the significant characteristics that allow AR systems to accomplish degrees of efficacy in autism treatment. The review ends by classifying the various AR systems based on different criteria. The second part of the paper focuses on our new AR system as a case study. It covers the design considerations and decisions as well as the key features and appearance of the system. The paper concludes by making some recommendations for the further development of an AR system for application in the domain of child autism.

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References

  1. Akçayır M, Akçayır G, Pektaş HM, Ocak MA (2016) Augmented reality in science laboratories: the effects of augmented reality on university students’ laboratory skills and attitudes toward science laboratories. Computers in Human Behavior 57:334–342

    Google Scholar 

  2. Alkhamisi AO, Monowar MM (2013) Rise of augmented reality: current and future application areas. Int J Internet Distributed Sys 1(04):25–34

    Google Scholar 

  3. Alsaggaf EA, Baaisharah SS (2014) Directions of autism diagnosis by electroencephalogram based brain computer interface: a review. Life Sci J 11(6):298–304

    Google Scholar 

  4. Ames C, Fletcher-Watson S (2010) A review of methods in the study of attention in autism. Dev Rev 30(1):52–73

    Google Scholar 

  5. Anam K, Al-Jumaily A (2017) Evaluation of extreme learning machine for classification of individual and combined finger movements using electromyography on amputees and non-amputees. Neural Netw 85:51–68

    Google Scholar 

  6. Aresti-Bartolome N, Garcia-Zapirain B (2014) Technologies as support tools for persons with autistic spectrum disorder: a systematic review. Int J Environmental Res Public Health 11(8):7767–7802

    Google Scholar 

  7. Ashburner J, Vickerstaff S, Beetge J, Copley J (2016) Remote versus face-to-face delivery of early intervention programs for children with autism spectrum disorders: perceptions of rural families and service providers. Research in Autism Spectrum Disorders 23:1–14

    Google Scholar 

  8. Ashwin C, Chapman E, Colle L, Baron-Cohen S (2006) Impaired recognition of negative basic emotions in autism: a test of the amygdala theory. Social Neurosci 1 (3-4):349–363

    Google Scholar 

  9. Aung YM, Al-Jumaily A (2012) sEMG based ANN for shoulder angle prediction. Procedia Eng 41:1009–1015

    Google Scholar 

  10. Azuma R, Baillot Y, Behringer R, Feiner S, Julier S, MacIntyre B (2001) Recent advances in augmented reality. IEEE Comput Graphics Appl 21(6):34–47

    Google Scholar 

  11. Azuma RT (1997) A survey of augmented reality. Presence: Teleoperators & Virtual Environments 6(4):355–385

    Google Scholar 

  12. Bai Z (2012) Augmenting imagination for children with autism. In: Proceedings of the 11th international conference on interaction design and children, June 12-15. ACM, Bremen, pp 327–330

  13. Bai Z, Blackwell AF, Coulouris G (2012) Making pretense visible and graspable: an augmented reality approach to promote pretend play. In: 2012 IEEE international symposium on mixed and augmented reality (ISMAR), 5-8 Nov, Atlanta, GA, USA. IEEE, pp 267–268

  14. Bai Z, Blackwell A, Coulouris G (2015) Using augmented reality to elicit pretend play for children with autism. IEEE Trans Vis Comput Graphics 21(5):598–610

    Google Scholar 

  15. Balakrishnama S, Ganapathiraju A (1998) Linear discriminant analysis-a brief tutorial. Institute for Signal and Information Processing 18:1–8

    Google Scholar 

  16. Baron-Cohen S (2001) Theory of mind in normal development and autism. Prisme 34(1):74–183

    Google Scholar 

  17. Bhatt S, De Leon N, Al-Jumaily A (2014) Augmented reality game therapy for children with autism spectrum disorder. International Journal on Smart Sensing and Intelligent Systems 7(2):519–536

    Google Scholar 

  18. Billard A, Robins B, Nadel J, Dautenhahn K (2007) Building Robota, a mini-humanoid robot for the rehabilitation of children with autism. Assist Technol 19 (1):37–49

    Google Scholar 

  19. Bimber O, Raskar R (2005) Augmented reality displays. In: Spatial augmented reality: merging real and virtual worlds, AK Peters/CRC Press, pp 1–361

  20. Boccanfuso L, O’kane JM (2010) Adaptive robot design with hand and face tracking for use in autism therapy. In: International conference on social robotics, 23-24 November. Springer, Singapore, pp 265–274

  21. Boucher J, Wolfberg P (2003) Aims and design of the special issue. Autism 7:339–346

    Google Scholar 

  22. Brandão J, Cunha P, Vasconcelos J, Carvalho V, Soares F (2015) An augmented reality gamebook for children with autism spectrum disorders. United States of America, New York

    Google Scholar 

  23. Buescher AV, Cidav Z, Knapp M, Mandell DS (2014) Costs of autism spectrum disorders in the United Kingdom and the United States. JAMA Pediatrics 168(8):721–728

    Google Scholar 

  24. Burges CJ (1998) A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 2(2):121–167

    Google Scholar 

  25. Cai Y, Chia NK, Thalmann D, Kee NK, Zheng J, Thalmann NM (2013) Design and development of a virtual dolphinarium for children with autism. IEEE Trans Neural Sys Rehabilitation Eng 21(2):208–217

    Google Scholar 

  26. Carmigniani J, Furht B (2011) Augmented reality: an overview. In: Handbook of augmented reality. Springer, New York, pp 3–46

  27. Carmigniani J, Furht B, Anisetti M, Ceravolo P, Damiani E, Ivkovic M (2011) Augmented reality technologies, systems and applications. Multimed Tools Appl 51(1):341–377

    Google Scholar 

  28. Casas X, Herrera G, Coma I, Fernández M (2012) A Kinect-based augmented reality system for individuals with autism spectrum disorders. In: Grapp/ivapp, 24-26 February, Rome, Italy, pp 440–446

  29. Chen CH, Lee IJ, Lin LY (2015) Augmented reality-based self-facial modeling to promote the emotional expression and social skills of adolescents with autism spectrum disorders. Research in Developmental Disabilities 36:396–403

    Google Scholar 

  30. Chen CH, Lee IJ, Lin LY (2016) Augmented reality-based video-modeling storybook of nonverbal facial cues for children with autism spectrum disorder to improve their perceptions and judgments of facial expressions and emotions. Computers in Human Behavior 55:477–485

    Google Scholar 

  31. Cidav Z, Marcus SC, Mandell DS (2012) Implications of childhood autism for parental employment and earnings. Pediatrics 129(4):617–623

    Google Scholar 

  32. Cihak DF, Moore EJ, Wright RE, McMahon DD, Gibbons MM, Smith C (2016) Evaluating augmented reality to complete a chain task for elementary students with autism. J Special Educ Technol 31(2):99–108

    Google Scholar 

  33. Cook AM, Adams K, Volden J, Harbottle N, Harbottle C (2011) Using Lego robots to estimate cognitive ability in children who have severe physical disabilities. Disability and Rehabilitation: Assistive Technology 6(4):338–346

    Google Scholar 

  34. Corrêa AGD, Corrêa AGD (2004) A Criatividade através da expressão musical: uma interface gestual para composição musical interativa. RENOTE 2 (2):1–10

    Google Scholar 

  35. Crippa A, Salvatore C, Perego P, Forti S, Nobile M, Molteni M, Castiglioni I (2015) Use of machine learning to identify children with autism and their motor abnormalities. Journal of Autism and Developmental Disorders 45(7):2146–2156

    Google Scholar 

  36. Cunha P, Brandão J, Vasconcelos J, Soares F, Carvalho V (2016) Augmented reality for cognitive and social skills improvement in children with ASD. In: 2016 13th international conference remote engineering and virtual instrumentation (REV). IEEE, pp 334–335

  37. Datta S (2016) Machine-learning with openCV. In: Learning OpenCV 3 application development, Packt Publishing Ltd, pp 1–310

  38. Dawson G, Osterling J, Meltzoff AN, Kuhl P (2000) Case study of the development of an infant with autism from birth to two years of age. J Appl Dev Psychol 21(3):299–313

    Google Scholar 

  39. Dillenburger K, McKerr L, Jordan JA (2014) Lost in translation: public policies, evidence-based practice, and autism spectrum disorder. Int J Disability Develop Educ 61(2):134–151

    Google Scholar 

  40. Dudley C, Emery J (2014) The value of caregiver time: costs of support and care for individuals living with autism spectrum disorder. SPP Res Paper 7(1):1–48

    Google Scholar 

  41. Dunleavy M, Dede C (2014) Augmented reality teaching and learning. In: Handbook of research on educational communications and technology. Springer, New York, pp 735–745

  42. Duquette A, Mercier H, Michaud F (2006) Investigating the use of a mobile robotic toy as an imitation agent for children with autism. In: Proceedings international conference on epigenetic robotics: modeling cognitive development in robotic systems, Paris, France, pp 167–168

  43. Dwinnell W, Sevis D, Matlab Central (2010) LDA: linear discriminant analysis. https://www.mathworks.com/matlabcentral/fileexchange/29673-lda-linear-discriminant-analysis?focused=5174518&tab=function

  44. El-Seoud M, Halabi O, Geroimenko V (2019) Assisting individuals with autism and cognitive disorders: an augmented reality based framework. Int J Online Eng 15 (4):28–39

    Google Scholar 

  45. Escobedo L, Nguyen DH, Boyd L, Hirano S, Rangel A, Garcia-Rosas D, Tentori M, Hayes G (2012) MOSOCO: a mobile assistive tool to support children with autism practicing social skills in real-life situations. In: Proceedings of the SIGCHI conference on human factors in computing systems, 05 - 10 May, Austin, Texas, USA. ACM, pp 2589–2598

  46. Escobedo L, Tentori M, Quintana E, Favela J, Garcia-Rosas D (2014) Using augmented reality to help children with autism stay focused. IEEE Pervasive Comput 13(1):38–46

    Google Scholar 

  47. Favela J, Kaye J, Skubic M, Rantz M, Tentori M (2015) Living labs for pervasive healthcare research. IEEE Pervasive Comput 14(2):86–89

    Google Scholar 

  48. Fernell E, Eriksson MA, Gillberg C (2013) Early diagnosis of autism and impact on prognosis: a narrative review. Clinical Epidemiology 5(1):33–43

    Google Scholar 

  49. Fletcher T (2009) Support vector machines explained. Tutorial paper, 4. http://sutikno.blog.undip.ac.id/files/2011/11/SVM-Explained.pdf

  50. Focaroli V, Taffoni F, Parsons SM, Keller F, Iverson JM (2016) Performance of motor sequences in children at heightened vs. low risk for ASD: a longitudinal study from 18 to 36 months of age. Frontiers Psychol 7:1–9

    Google Scholar 

  51. Fountain C, King MD, Bearman PS (2011) Age of diagnosis for autism: individual and community factors across 10 birth cohorts. J Epidemiol Community Health 65 (6):503–510

    Google Scholar 

  52. Galna B, Barry G, Jackson D, Mhiripiri D, Olivier P, Rochester L (2014) Accuracy of the Microsoft Kinect sensor for measuring movement in people with Parkinson’s disease. Gait & Posture 39(4):1062–1068

    Google Scholar 

  53. Ganz ML (2007) The lifetime distribution of the incremental societal costs of autism. Archives of Pediatrics & Adolescent Medicine 161(4):343–349

    Google Scholar 

  54. Geroimenko V (2012) Augmented reality technology and art: the analysis and visualization of evolving conceptual models. In: 16th international conference on information visualisation, 11 - 13 July, Washington, DC, USA. IEEE, pp 445–453

  55. Ghose T, Namboodiri V, Pendse R (2015) Thin is green: leveraging the thin-client paradigm for sustainable mobile computing. Comput Electrical Eng 45:155–168

    Google Scholar 

  56. Giullian N, Ricks D, Atherton A, Colton M, Goodrich M, Brinton B (2010) Detailed requirements for robots in autism therapy. In: IEEE international conference on systems, man and cybernetics, 10-13 October, Istanbul, Turkey. IEEE, pp 2595–2602

  57. Greczek J, Kaszubski E, Atrash A, Matarić M (2014) Graded cueing feedback in robot-mediated imitation practice for children with autism spectrum disorders. In: The 23rd IEEE international symposium on robot and human interactive communication, 25-29 Aug, Edinburgh, UK. IEEE, pp 561–566

  58. Gresham FM, Elliott SN (1987) The relationship between adaptive behavior and social skills issues in definition and assessment. The J Special Educ 21(1):167–181

    Google Scholar 

  59. Hadwin J, Baron-Cohen S, Howlin P, Hill K (1996) Can we teach children with autism to understand emotions, belief, or pretence? Dev Psychopathol 8(2):345–365

    Google Scholar 

  60. Hailpern J, Karahalios K, DeThorne L, Halle J (2010) Vocsyl: visualizing syllable production for children with ASD and speech delays. In: Proceedings of the 12th international ACM SIGACCESS conference on Computers and accessibility, 25 - 27 October, Orlando, Florida, USA. ACM, pp 297–298

  61. Hashemi J, Spina TV, Tepper M, Esler A, Morellas V, Papanikolopoulos N, Sapiro G (2012) A computer vision approach for the assessment of autism-related behavioral markers. In: 2012 IEEE international conference on development and learning and epigenetic robotics (ICDL 2012), 7-9 November, San Diego, California, USA. IEEE, pp 1–7

  62. HCII, The Human-Computer Interaction Institute (2014) Scaffolding science achievement in a culturally diverse classroom: bridging the gap with virtual peers. http://articulab.hcii.cs.cmu.edu/projects/alex/

  63. Helen G (1997) Educational software: criteria for evaluation. In: The Australian society for computers in learning in tertiary education (ASCILITE), 7 -10 December, Perth, Western Australia, Australia, pp 219–223

  64. Herbst I, Braun AK, McCall R, Broll W (2008) Timewarp: interactive time travel with a mobile mixed reality game. In: Proceedings of the 10th international conference on Human computer interaction with mobile devices and services, 02 - 05 September, Amsterdam, The Netherlands. ACM, pp 235–244

  65. Herrera G, Casas X, Sevilla J, Rosa L, Pardo C, Plaza J, Jordan R, Le Groux S (2012) Pictogram room: natural interaction technologies to aid in the development of children with autism. Annuary of Clinical and Health Psychology 8:39–44

    Google Scholar 

  66. Huang GB, Zhu QY, Siew CK (2004) Extreme learning machine: a new learning scheme of feedforward neural networks. Neural Netw 2(3):985–990

    Google Scholar 

  67. Huang GB, Zhu QY, Siew CK (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1-3):489–501

    Google Scholar 

  68. Hugues O, Fuchs P, Nannipieri O (2011) New augmented reality taxonomy: technologies and features of augmented environment. In: Handbook of augmented reality. Springer, New York, pp 47–63

  69. Hung SH, Hsiao SW, Teng YC, Chien R (2015) Real-time and intelligent private data protection for the Android platform. Pervasive and Mobile Computing 24:231–242

    Google Scholar 

  70. López de Ipiña D, Mendonça PR, Hopper A (2002) TRIP: a low-cost vision-based location system for ubiquitous computing. Pers Ubiquit Comput 6(3):206–219

    Google Scholar 

  71. Jackson S (2016) The art of neural networks using c sharp. CreateSpace Independent Publishing Platform, pp 1–128

  72. Järbrink K (2007) The economic consequences of autistic spectrum disorder among children in a Swedish municipality. Autism 11(5):453–463

    Google Scholar 

  73. Järbrink K, McCrone P, Fombonne E, Zandén H, Knapp M (2007) Cost-impact of young adults with high-functioning autistic spectrum disorder. Research in Developmental Disabilities 28(1):94–104

    Google Scholar 

  74. Johnson CP, Myers SM (2007) Identification and evaluation of children with autism spectrum disorders. Pediatrics 120(5):1183–1215

    Google Scholar 

  75. Karimi HA (2011) Introduction to navigation. In: Universal navigation on smartphones. Springer, pp 1–16

  76. Kato H, Billinghurst M (1999) Marker tracking and hmd calibration for a video-based augmented reality conferencing system. In: Proceedings 2nd IEEE and ACM international workshop on augmented reality (IWAR’99), 20 - 21 October, San Francisco, CA, USA. IEEE, pp 85–94

  77. Kim J, Kim J, Jang GJ, Lee M (2017) Fast learning method for convolutional neural networks using extreme learning machine and its application to lane detection. Neural Netw 87:109–121

    Google Scholar 

  78. King M, Bearman P (2009) Diagnostic change and the increased prevalence of autism. Int J Epidemiol 38(5):1224–1234

    Google Scholar 

  79. King MD, Bearman PS (2011) Socioeconomic status and the increased prevalence of autism in California. Am Sociol Rev 76(2):320–346

    Google Scholar 

  80. Knapp M, Romeo R, Beecham J (2007) The economic consequences of autism in the UK. Foundation for People with Learning Disabilities, pp 1–27

  81. Knapp M, Romeo R, Beecham J (2009) Economic cost of autism in the UK. Autism 13(3):317–336

    Google Scholar 

  82. Koegel LK, Koegel RL, Ashbaugh K, Bradshaw J (2014) The importance of early identification and intervention for children with or at risk for autism spectrum disorders. Int J Speech-Language Pathol 16(1):50–56

    Google Scholar 

  83. Kohavi R (1995) A study of cross-validation and bootstrap for accuracy estimation and model selection. In: The 1995 international joint conference on AI IJCAI, 20-25 August, Montreal, Canada, 2, vol 14, pp 1137–1145

  84. Krantz PJ, McClannahan LE (1998) Social interaction skills for children with autism: a script-fading procedure for beginning readers. J Appl Behavior Analysis 31 (2):191–202

    Google Scholar 

  85. Lakshmiprabha N, Santos A, Mladenov D, Beltramello O (2014) An augmented and virtual reality system for training autistic children. In: IEEE international symposium on mixed and augmented reality (ISMAR), 10-12 September, Munich, Germany. IEEE, pp 277–278

  86. Lavelle TA, Weinstein MC, Newhouse JP, Munir K, Kuhlthau KA, Prosser LA (2014) Economic burden of childhood autism spectrum disorders. Pediatrics 133 (3):520–529

    Google Scholar 

  87. Lee K (2012) Augmented reality in education and training. TechTrends 56 (2):13–21

    Google Scholar 

  88. Li KH, Lou SJ, Tsai HY, Shih RC (2012) The effects of applying game-based learning to webcam motion sensor games for autistic students’ sensory integration training. Turkish Online Journal of Educational Technology-TOJET 11(4):451–459

    Google Scholar 

  89. Liarokapis F, Anderson EF (2010) Using augmented reality as a medium to assist teaching in higher education. In: Eurographics 2010, 3-7 May, Norrköping, Sweden. Eurographics Association, pp 9–16

  90. Lima JP, Simões F, Figueiredo L, Kelner J (2010) Model based markerless 3D tracking applied to augmented reality. Journal on 3D Interactive Systems 1:2–15

    Google Scholar 

  91. Lin CY, Chai HC, Jy Wang, Chen CJ, Liu YH, Chen CW, Lin CW, Huang YM (2015) Augmented reality in educational activities for children with disabilities. Displays 42:51–54

    Google Scholar 

  92. Liu R, Salisbury JP, Vahabzadeh A, Sahin NT (2017) Feasibility of an autism-focused augmented reality smartglasses system for social communication and behavioral coaching. Frontiers in Pediatrics 5:145

    Google Scholar 

  93. Liu X, Fu H (2014) PSO-based support vector machine with cuckoo search technique for clinical disease diagnoses. Sci World J 6:1–7

    Google Scholar 

  94. Lorenzo G, Gómez-Puerta M, Arráez-Vera G, Lorenzo-Lledó A (2019) Preliminary study of augmented reality as an instrument for improvement of social skills in children with autism spectrum disorder. Educ Inform Technol 24(1):181–204

    Google Scholar 

  95. Mackay WE (1998) Augmented reality: linking real and virtual worlds: a new paradigm for interacting with computers. In: Proceedings of the working conference on advanced visual interfaces, 25 - 27 May, L’Aquila, Italy. ACM, pp 13–21

  96. Markowitz PI (1983) Autism in a child with congenital cytomegalovirus infection. Journal of Autism and Developmental Disorders 13(3):249–253

    Google Scholar 

  97. Mazzei D, Billeci L, Armato A, Lazzeri N, Cisternino A, Pioggia G, Igliozzi R, Muratori F, Ahluwalia A, De Rossi D (2010) The face of autism. In: 19th RO-MAN 2010, 13-15 September, Viareggio, Italy. IEEE, pp 791–796

  98. McMahon D, Cihak DF, Wright R (2015a) Augmented reality as a navigation tool to employment opportunities for postsecondary education students with intellectual disabilities and autism. Journal of Research on Technology in Education 47(3):157–172

    Google Scholar 

  99. McMahon DD, Smith CC, Cihak DF, Wright R, Gibbons MM (2015b) Effects of digital navigation aids on adults with intellectual disabilities: comparison of paper map, Google maps, and augmented reality. J Special Educ Technol 30(3):157–165

    Google Scholar 

  100. McMahon DD, Cihak DF, Wright RE, Bell SM (2016) Augmented reality for teaching science vocabulary to postsecondary education students with intellectual disabilities and autism. J Res Technol Educ 48(1):38–56

    Google Scholar 

  101. Mekni M, Lemieux A (2014) Augmented reality: applications, challenges and future trends. In: et al. (ed) Applied computational science: proceedings of the 13th international conference on applied computer and applied computational science (ACACOS ’14), 23-25 April, Kuala Lumpur, Malaysia, pp 205–214

  102. Mendoza RL (2010) The economics of autism in Egypt. American Journal of Economics and Business Administration 2(1):12–19

    Google Scholar 

  103. Michel P (2004) The use of technology in the study, diagnosis and treatment of autism. Final term Paper for CSC350: Autism and Associated Developmental Disorders, pp 1–26

  104. Microsoft (2017a) Kinect for windows. https://developer.microsoft.com/en-us/windows/kinect

  105. Microsoft (2017b) Microsoft Kinect joint type. https://msdn.microsoft.com/en-us/library/microsoft.kinect.jointtype.aspx

  106. Mieke N (2012) School of engineering, design and technology University of Bradford. An overview of augmented reality technologies. In: Research seminar series workshop, 18 April, Bradford, England, UK, 11th, pp 142–147

  107. Milgram P, Takemura H, Utsumi A, Kishino F (1995) Augmented reality: a class of displays on the reality-virtuality continuum. In: Telemanipulator and telepresence technologies, 21 December, vol 2351. International Society for Optics and Photonics, pp 282–293

  108. Mitchell E, Monaghan D, O’Connor NE (2013) Classification of sporting activities using smartphone accelerometers. Sensors 13(4):5317–5337

    Google Scholar 

  109. Munson J, Pasqual P (2012) Using technology in autism research: the promise and the perils. Computer 45(6):89–91

    Google Scholar 

  110. Mythili M, Shanavas AM (2014) A study on Autism spectrum disorders using classification techniques. International Journal of Soft Computing and Engineering IJSCE 4(5):88–91

    Google Scholar 

  111. Noble WS (2006) What is a support vector machine? Nature biotechnology 24 (12):1565–1567

    Google Scholar 

  112. Otte K, Kayser B, Mansow-Model S, Verrel J, Paul F, Brandt AU, Schmitz-Hübsch T (2016) Accuracy and reliability of the kinect version 2 for clinical measurement of motor function. PloS one 11(11):e0166,532

    Google Scholar 

  113. Park H, Park JI (2004) Invisible marker tracking for AR. In: Proceedings of the 3rd IEEE/ACM international symposium on mixed and augmented reality, 02 - 05 November, Arlington, VA, USA. IEEE Computer Society, pp 272–273

  114. Pashler HE, Sutherland S (1998) Introduction. In: The psychology of attention, vol 15. MIT Press, Cambridge, pp 1–322

  115. Pettersson C, Schmidt E (2014) Challenges with session to session management in brain computer interfaces: a comparison of classification methods for motor imagery induced EEG patterns. http://www.diva-portal.org/smash/get/diva2:771132/FULLTEXT01.pdf

  116. Phan VT, Choo SY (2010) Interior design in augmented reality environment. Int J Comput Appl 5(5):16–21

    Google Scholar 

  117. Philip RCM (2009) Emotion processing in autism spectrum disorder. Doctoral thesis, University of Edinburgh. https://www.era.lib.ed.ac.uk/bitstream/handle/1842/4216/Philip2009.pdf?sequence=2&isAllowed=y

  118. Prinz J (2004) Which emotions are basic. In: Evans D, Cruse P (eds) Emotion, evolution, and rationality, vol 69. Oxford University Press, pp 1–288

  119. Quintana E, Ibarra C, Escobedo L, Tentori M, Favela J (2012) Object and gesture recognition to assist children with autism during the discrimination training. In: Iberoamerican congress on pattern recognition, 3-6 September, Buenos Aires, Argentina. Springer, pp 877–884

  120. Raajan N, Suganya S, Priya M, Ramanan SV, Janani S, Nandini NS, Hemanand R, Gayathri S (2012) Augmented reality based virtual reality. Procedia Engineering 38:1559–1565

    Google Scholar 

  121. Radhakrishnan S (2015) Mobile augmented reality in modern libraries: prospects and challenges. Indian J Sci 21(73):385–388

    Google Scholar 

  122. Richard E, Billaudeau V, Richard P, Gaudin G (2007) Augmented reality for rehabilitation of cognitive disabled children: a preliminary study. In: 2007 Virtual rehabilitation, 27-29 September, Venice, Italy. IEEE, pp 102–108

  123. Ricks DJ, Colton MB, Goodrich MA (2010) Design and evaluation of a clinical upper-body humanoid robot for autism therapy. In: Proceedings of the 2010 international conference on applied bionics and biomechanics, 14-16 October, Venice, Italy, pp 14–16

  124. Ringland KE, Zalapa R, Neal M, Escobedo L, Tentori M, Hayes GR (2014) Sensorypaint: a multimodal sensory intervention for children with neurodevelopmental disorders. In: Proceedings of the 2014 ACM international joint conference on pervasive and ubiquitous computing, 13-17 September, Seattle, WA, USA. ACM, pp 873–884

  125. Roth L (2013) Autism spectrum disorder. NSW Parliamentary Research Service, pp 1–40. https://www.parliament.nsw.gov.au/researchpapers/Documents/autism-spectrum-disorder/Autism

  126. Russell RA, Taylor G, Kleeman L, Purnamadjaja AH (2004) Multi-sensory synergies in humanoid robotics. Int J Humanoid Robotics 1(2):289–314

    Google Scholar 

  127. Sahin N, Keshav N, Salisbury J, Vahabzadeh A (2018a) Safety and lack of negative effects of wearable augmented-reality social communication aid for children and adults with autism. J Clinical Med 7(8):188–205

    Google Scholar 

  128. Sahin NT, Abdus-Sabur R, Caffo BS, Liu R, Salisbury JP, Vahabzadeh A (2018b) Augmented reality intervention for social communication in autism in a school classroom: rated by teachers and parents as effective and usable in a controlled. Longitudinal Pilot Study PsyArXiv, pp 1–10

  129. Satsangi R, Miller B (2017) The case for adopting virtual manipulatives in mathematics education for students with disabilities. Preventing School Failure: Alternative Education for Children and Youth 61(4):303–310

    Google Scholar 

  130. Scassellati B, Admoni H, Matarić M (2012) Robots for use in autism research. Annu Rev Biomed Eng 14:275–294

    Google Scholar 

  131. Scherer KR (1987) Toward a dynamic theory of emotion. Geneva Studies in Emotion 1:1–96

    Google Scholar 

  132. Sharpe DL, Baker DL (2011) The financial side of autism: private and public costs. In: Mohammadi MR (ed) A comprehensive book on autism spectrum disorders, InTech, pp 275–286

  133. Sigman M, Spence SJ, Wang AT (2006) Autism from developmental and neuropsychological perspectives. Annu Rev Clin Psychol 2(1):327–355

    Google Scholar 

  134. Sree MS, Durga S, Sindhusha P (2013) Augmented reality. Int J Sci Eng Res 4(9):1469–1474

    Google Scholar 

  135. Sutherland IE (1965) The ultimate display. Multimedia: From Wagner to Virtual Reality 2:506–508

    Google Scholar 

  136. Taffoni F, Focaroli V, Keller F, Iverson J (2014) A technological approach to studying motor planning ability in children at high risk for ASD. In: 36Th annual international conference of the IEEE engineering in medicine and biology society, 26-30 Aug, Chicago, IL, USA. IEEE, pp 3638–3641

  137. Tang A, Biocca F, Lim L (2004) Comparing differences in presence during social interaction in augmented reality versus virtual reality environments: an exploratory study. In: et al (ed) 7th annual international workshop on presence (PRESENCE 2004), 13-15 October, Valencia, Spain, pp 204–208

  138. Tang TY, Xu J, Winoto P (2019) Automatic object recognition in a light-weight augmented reality-based vocabulary learning application for children with autism. In: Proceedings of the 2019 3rd international conference on innovation in artificial intelligence, 15 - 18 March, Suzhou, China. ACM, pp 65–68

  139. Tentori M, Escobedo L, Balderas G (2015) A smart environment for children with autism. IEEE Pervasive Comput 14(2):42–50

    Google Scholar 

  140. Veeraraghavan S, Srinivasan K (2007) Exploration of autism expert systems. In: Fourth international conference on information technology (ITNG’07), 2 - 4 April, Las Vegas, Nevada, USA. IEEE, pp 261–264

  141. Vullamparthi AJ, Nelaturu SCB, Mallaya DD, Chandrasekhar S (2013) Assistive learning for children with autism using augmented reality. In: 2013 IEEE fifth international conference on technology for education (t4e), 18 - 20 December, Kharagpur, India. IEEE, pp 43–46

  142. Wainer J, Dautenhahn K, Robins B, Amirabdollahian F (2010) Collaborating with kaspar: using an autonomous humanoid robot to foster cooperative dyadic play among children with autism. In: 10th IEEE-RAS international conference on humanoid robots, humanoids 2010, 6-8 December, Nashville, TN, USA. IEEE, pp 631–638

  143. Wang M, Reid D (2011) Virtual reality in pediatric neurorehabilitation: attention deficit hyperactivity disorder, autism and cerebral palsy. Neuroepidemiology 36(1):2–18

    Google Scholar 

  144. Wedyan M, Al-Jumaily A (2016) Upper limb motor coordination based early diagnosis in high risk subjects for Autism. In: 2016 IEEE symposium series on computational intelligence (SSCI), 6-9 December, Athens, Greece. IEEE, pp 1–8

  145. Wedyan M, Al-Jumaily A (2017) An investigation of upper limb motor task based discriminate for high risk autism. In: International conference on intelligent systems and knowledge engineering (ISKE 2017), 24-26 November, Nanjing, China, IEEE, 12th, pp 1–6

  146. Wedyan M, Al-Jumaily A, Crippa A (2018) Early diagnose of autism spectrum disorder using machine learning based on simple upper limb movements. In: International conference on hybrid intelligent systems, 13 - 15 December, Porto, Portugal, Springer, 18th, pp 491–500

  147. Weiss MJ, Harris SL (2001) Teaching social skills to people with autism. Behavior Modification 25(5):785–802

    Google Scholar 

  148. Wolfberg P, Bottema-Beutel K, DeWitt M (2012) Including children with autism in social and imaginary play with typical peers: integrated play groups model. American Journal of Play 5(1):55–80

    Google Scholar 

  149. Wu HK, Lee SWY, Chang HY, Liang JC (2013) Current status, opportunities and challenges of augmented reality in education. Computers & Education 62:41–49

    Google Scholar 

  150. Xanthopoulos P, Pardalos PM, Trafalis TB (2013) Linear discriminant analysis. In: Robust data mining. Springer, pp 27–33

  151. Yantaç AE, Çorlu D, Fjeld M, Kunz A (2015) Exploring diminished reality (DR) spaces to augment the attention of individuals with autism. In: Proceedings of the 2015 IEEE international symposium on mixed and augmented reality workshops, 29 September - 03 October, Fukuoka, Japan. IEEE, pp 68–73

  152. Yee HSS (2012) Mobile technology for children with autism spectrum disorder: major trends and issues. In: IEEE symposium on e-learning, e-management and e-services, IS3e, 21-24, Oct, Kuala Lumpur, Malaysia. IEEE, pp 1–5

  153. You S, Neumann U, Azuma R (1999) Hybrid inertial and vision tracking for augmented reality registration. In: Proceedings IEEE virtual reality, 13-17 March, Houston, TX, USA. IEEE, pp 260–267

  154. You ZH, Lei YK, Zhu L, Xia J, Wang B (2013) Prediction of protein-protein interactions from amino acid sequences with ensemble extreme learning machines and principal component analysis. BMC Bioinformatics 14(8):S10–21

    Google Scholar 

  155. Yuen SCY, Yaoyuneyong G, Johnson E (2011) Augmented reality: an overview and five directions for AR in education. J Educ Technol Development Exchange (JETDE) 4(1):119–140

    Google Scholar 

  156. Zaidan A, Zaidan B, Hussain M, Haiqi A, Kiah MM, Abdulnabi M (2015) Multi-criteria analysis for OS-EMR software selection problem: a comparative study. Decision Support Systems 78:15–27

    Google Scholar 

  157. Zhou F, Duh HBL, Billinghurst M (2008) Trends in augmented reality tracking, interaction and display: a review of ten years of ISMAR. In: Proceedings of the 7th IEEE/ACM international symposium on mixed and augmented reality, 15-18 September, Cambridge, UK. IEEE Computer Society, pp 193–202

  158. Zhu QY, Qin AK, Suganthan PN, Huang GB (2005) Evolutionary extreme learning machine. Pattern Recogn 38(10):1759–1763

    MATH  Google Scholar 

  159. Zhu W, Zeng N, Wang N (2010) Sensitivity, specificity, accuracy, associated confidence interval and ROC analysis with practical SAS implementations. In: et al (ed) NESUG proceedings: health care and life sciences, 14-17 Nov, Baltimore, Maryland, pp 1–9

  160. Zwaigenbaum L, Bryson S, Rogers T, Roberts W, Brian J, Szatmari P (2005) Behavioral manifestations of autism in the first year of life. Int J Developmental Neurosci 23(2-3):143–152

    Google Scholar 

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Correspondence to Mohammad Wedyan or Adel AL-Jumaily.

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This study was produced as part of the “Implementation of an Augmented Reality Game to Track Upper Limb Movement in Autistic Children,” approval number ETH18-2710 approved by the University of Technology, Sydney (Sydney, New South Wales, Australia).

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Wedyan, M., AL-Jumaily, A. & Dorgham, O. The use of augmented reality in the diagnosis and treatment of autistic children: a review and a new system. Multimed Tools Appl 79, 18245–18291 (2020). https://doi.org/10.1007/s11042-020-08647-6

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