The Wall Lab at Stanford University


« back to news index

Will you use Digital Health to Contribute to Autism Research?

November 19, 2019

In the last decade, autism spectrum disorder (ASD) prevalence in the U.S. increased by over 200%, affecting 1 in 40 children.1,2 Autism is estimated to affect 2% of children worldwide,2,3 and has become one of the most pressing pediatric health concerns globally.4,5,6,7 Standard approaches to diagnosis, such as the Autism Diagnostic Observation Schedule (ADOS)8 and the Autism Diagnostic Interview (ADI-R),9,10 and to therapy, such as Applied Behavioral Analysis (ABA), are difficult to access due to shortages of clinical practitioners, particularly in lower-income countries.9,11,12 The average age of diagnosis in the U.S. is 5 years of age for high-income and 8 years of age for low-income families. 2,13,14 Roughly 27% of U.S. children over the age of 8 remain undiagnosed.15

There is a high urgency to resolve these widespread problems with access to care. Research has shown that behavioral therapy by 5 years of age 17-21 can lessen or even eliminate core autism deficits including restricted and/or repetitive behaviors, difficulty with language, poor social attention, inability to understand facial expressions, and disinterest with social interactions.17,19,22-28 Worse, the impact of the interventions degrades after the age of 5, and by 8 years of age, children often do not respond as effectively. Therefore, early detection and therapy are both of vital importance.

Arguably, the most potent way to address this health crisis is via digital technologies. Children with ASD have shown high engagement with gamified systems which present an exciting opportunity for the development of novel diagnostic and therapeutic platform.29 As one example, researchers at King’s College London recently developed an interactive game, ECHOES, which allowed children to acquire social communication and emotional regulation skills through guided interactions with an intelligent virtual avatar.30 Multiple similar interventions have emerged over the past decade leveraging the latest technological developments in multitouch and interactive interfaces,31-34 humanoid-robot design,35 virtual and augmented reality systems.36-38

Virtually all interactive technological developments rely on extensive libraries of labeled images of human emotion. Nonetheless, children are significantly underrepresented in these sources and thus the classifiers trained on these databases are not optimized for pediatric autism research.39 Embracing the challenge, our team recently developed a Charades-style mobile game, which engages children with autism and their families in a fun and convenient interaction that reinforces prosocial learning while simultaneously generating data for diagnostic and therapeutic AI development.39, 40 This is where you come in.

We need your help to develop the app’s capabilities! In an effort to corroborate and further increase GuessWhat?’s clinical efficacy, we are currently enrolling parents of children with Autism Spectrum Disorder (ASD) to participate in a fun research study!

Interested? Find out more by signing up on!

Not a parent of a child with ASD? We need you too! Help us by simply playing the app (available for iOS and Android) and sharing with friends and family.

To stay tuned for updates, follow us on


  1. Hertz-Picciotto I, Delwiche L. The Rise in Autism and the Role of Age at Diagnosis. Epidemiology 2009; 20(1): 84-90.
  2. Kogan MD, Vladutiu CJ, Schieve LA, et al. The Prevalence of Parent-Reported Autism Spectrum Disorder Among US Children. Pediatrics 2018; 142(6): e20174161.
  3. Hahler E-M, Elsabbagh M. Autism: A Global Perspective. Current Developmental Disorders Reports 2015; 2(1): 58-64.
  4. Dawson G. Why it’s important to continue universal autism screening while research fully examines its impact. JAMA pediatrics 2016; 170(6): 527-8.
  5. Piccininni C, Bisnaire L, Penner M. Cost-effectiveness of Wait Time Reduction for Intensive Behavioral Intervention Services in Ontario, Canada. JAMA Pediatrics 2017; 171(1): 23-30.
  6. Murray CJ, Lopez AD. Alternative projections of mortality and disability by cause 1990-2020: Global Burden of Disease Study. Lancet 1997; 349(9064): 1498-504.
  7. Whiteford HA, Degenhardt L, Rehm J, et al. Global burden of disease attributable to mental and substance use disorders: findings from the Global Burden of Disease Study 2010. The lancet 2013; 382(9904): 1575-86.
  8. Gotham K, Risi S, Pickles A, Lord C. The Autism Diagnostic Observation Schedule: revised algorithms for improved diagnostic validity. J Autism Dev Disord 2007; 37(4): 613-27.
  9. Lord C, Rutter M, Le Couteur A. Autism Diagnostic Interview-Revised: a revised version of a diagnostic interview for caregivers of individuals with possible pervasive developmental disorders. J Autism Dev Disord 1994; 24(5): 659-85.
  10. Poustka F, Lisch S, Ruhl D, Sacher A, Schmotzer G, Werner K. The standardized diagnosis of autism, Autism Diagnostic Interview-Revised: interrater reliability of the German form of the interview. Psychopathology 1996; 29(3): 145-53.
  11. Koegel LK, Koegel RL, Ashbaugh K, Bradshaw J. The importance of early identification and intervention for children with or at risk for autism spectrum disorders. International Journal of Speech-Language Pathology 2014; 16(1): 50-6.
  12. Lord C, Rutter M, DiLavore P, Risi S, Gotham K, Bishop S. Autism diagnostic observation schedule, (ADOS-2) modules 1-4. Los Angeles, California: Western Psychological Services 2012.
  13. Mazurek MO, Handen BL, Wodka EL, Nowinski L, Butter E, Engelhardt CR. Age at first autism spectrum disorder diagnosis: the role of birth cohort, demographic factors, and clinical features. J Dev Behav Pediatr 2014; 35(9): 561-9.
  14. Siklos S, Kerns KA. Assessing the diagnostic experiences of a small sample of parents of children with autism spectrum disorders. Res Dev Disabil 2007; 28(1): 9-22.
  15. Association AP. Diagnostic and statistical manual of mental disorders (DSM-5®). Arlington, VA: American Psychiatric Pub; 2013.
  16. Kamau LZ. Autism Spectrum Disorders (ASD) in Kenya: Barriers Encountered in Diagnosis, Treatment and Management. J Res Pharm Sci 2017; 3(7): 1-11.
  17. Dawson G. Early behavioral intervention, brain plasticity, and the prevention of autism spectrum disorder. Dev Psychopathol 2008; 20(3): 775-803.
  18. Dawson G, Jones EJH, Merkle K, et al. Early Behavioral Intervention Is Associated With Normalized Brain Activity in Young Children With Autism. Journal of the American Academy of Child and Adolescent Psychiatry 2012; 51(11): 1150-9.
  19. Dawson G, Rogers S, Munson J, et al. Randomized, controlled trial of an intervention for toddlers with autism: the Early Start Denver Model. Pediatrics 2010; 125(1): e17-23.
  20. Landa RJ. Efficacy of early interventions for infants and young children with, and at risk for, autism spectrum disorders. International Review of Psychiatry 2018; 30(1): 25-39.
  21. Phillips DA, Shonkoff JP. From neurons to neighborhoods: The science of early childhood development. Washington, D.C.: National Academies Press; 2000.
  22. Dawson G. Early behavioral intervention, brain plasticity, and the prevention of autism spectrum disorder. Development and psychopathology 2008; 20(03): 775-803.
  23. Dawson G, Webb SJ, McPartland J. Understanding the nature of face processing impairment in autism: insights from behavioral and electrophysiological studies. Dev Neuropsychol 2005; 27(3): 403-24.
  24. Howlin P, Goode S, Hutton J, Rutter M. Adult outcome for children with autism. J Child Psychol Psychiatry 2004; 45(2): 212-29.
  25. Landa RJ, Holman KC, Garrett-Mayer E. Social and communication development in toddlers with early and later diagnosis of autism spectrum disorders. Arch Gen Psychiatry 2007; 64(7): 853-64.
  26. Palumbo L, Burnett HG, Jellema T. Atypical emotional anticipation in high-functioning autism. Mol Autism 2015; 6(1): 47.
  27. Sasson NJ, Pinkham AE, Weittenhiller LP, Faso DJ, Simpson C. Context Effects on Facial Affect Recognition in Schizophrenia and Autism: Behavioral and Eye-Tracking Evidence. Schizophr Bull 2016; 42(3): 675-83.
  28. Xavier J, Vignaud V, Ruggiero R, Bodeau N, Cohen D, Chaby L. A Multidimensional Approach to the Study of Emotion Recognition in Autism Spectrum Disorders. Front Psychol 2015; 6: 1954.
  29. Kalantarian H, Washington P, Schwartz J, Daniels J, Haber N, Wall DP. Guess What? Towards Understanding Autism from Structured Video Using Facial Affect. J Healthcare Informatics Research 2019; 3: 43-66.
  30. Bernardini S, Porayska-Pomsta K, Smith TJ (2014) Echoes: an intelligent serious game for fostering social communication in children with autism. Inf Sci 264:41–60
  31. Battocchi A, Pianesi F, Tomasini D, Zancanaro M, Esposito G, Venuti P, Ben Sasson A, Gal E, Weiss PL (2009) Collaborative puzzle game: a tabletop interactive game for fostering collaboration in children with autism spectrum disorders (asd). In: Proceedings of the ACM International Conference on Interactive Tabletops and Surfaces, ACM, pp 197–204
  32. Feil-Seifer D, Matari´c MJ (2009) Toward socially assistive robotics for augmenting interventions for children with autism spectrum disorders. In: Experimental robotics, Springer, pp 201–210
  33. Gal E, Bauminger N, Goren-Bar D, Pianesi F, Stock O, Zancanaro M, Weiss PLT (2009) Enhancing social communication of children with high-functioning autism through a co-located interface. AI Soc 24(1):75
  34. Giusti L, Zancanaro M, Gal E, Weiss PLT (2011) Dimensions of collaboration on a tabletop interface for children with autism spectrum disorder. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, ACM, pp 3295–3304
  35. Scassellati B, Admoni H, Matari´c M (2012) Robots for use in autism research. Annu Rev Biomed Eng 14:275–294
  36. Daniels J, Schwartz J, Haber N, Voss C, Kline A, Fazel A,Washington P, De T, Feinstein C,Winograd T et al (2017) 5.13 Design and efficacy of a wearable device for social affective learning in children with autism. J Am Acad Child Adolesc Psychiatry 56(10):S257
  37. Voss C, Washington P, Haber N, Kline A, Daniels J, Fazel A, De T, McCarthy B, Feinstein C, Winograd T et al (2016) Superpower glass: delivering unobtrusive real-time social cues in wearable systems. In: Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct, ACM, pp 1218–1226
  38. Washington P, Voss C, Kline A, Haber N, Daniels J, Fazel A, De T, Feinstein C, Winograd T, Wall D (2017) Superpowerglass: a wearable aid for the at-home therapy of children with autism. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 1(3):112
  39. Kalantarian H, Jedoui K, Washington P, Wall DP. A Mobile Game for Automatic Emotion-Labeling of Images. IEEE Transactions on Games 2018.
  40. Kalantarian H, Washington P, Schwartz J, Daniels J, Haber N, Wall DP. Guess What? Journal of Healthcare Informatics Research 2018.

tweet share