A proposal to act on Theory of Mind by applying robotics and virtual worlds with children with ASD

The article proposes an intervention framed under a single-subject research design where robotics and a 3D virtual environment are used jointly to improve the development of Theory of Mind in children with ASD. The project aims at verifying if the use of a humanoid robot, with high interactive abilities and responses, along with a virtual robot in a social virtual world can enable an improved comprehension of emotions and perspective taking. Specifically the planned activities are designed to gradually support the subjects with ASD in interactional settings in order to make them acquire the needed self-confidence to finally interact with a classmate in the virtual environment

Bridging the gap between emotion and joint action

Our daily human life is filled with a myriad of joint action moments, be it children playing, adults working together (i.e., team sports), or strangers navigating through a crowd. Joint action brings individuals (and embodiment of their emotions) together, in space and in time. Yet little is known about how individual emotions propagate through embodied presence in a group, and how joint action changes individual emotion. In fact, the multi-agent component is largely missing from neuroscience-based approaches to emotion, and reversely joint action research has not found a way yet to include emotion as one of the key parameters to model socio-motor interaction. In this review, we first identify the gap and then stockpile evidence showing strong entanglement between emotion and acting together from various branches of sciences. We propose an integrative approach to bridge the gap, highlight five research avenues to do so in behavioral neuroscience and digital sciences, and address some of the key challenges in the area faced by modern societies

Affective Computing

This book provides an overview of state of the art research in Affective Computing. It presents new ideas, original results and practical experiences in this increasingly important research field. The book consists of 23 chapters categorized into four sections. Since one of the most important means of human communication is facial expression, the first section of this book (Chapters 1 to 7) presents a research on synthesis and recognition of facial expressions. Given that we not only use the face but also body movements to express ourselves, in the second section (Chapters 8 to 11) we present a research on perception and generation of emotional expressions by using full-body motions. The third section of the book (Chapters 12 to 16) presents computational models on emotion, as well as findings from neuroscience research. In the last section of the book (Chapters 17 to 22) we present applications related to affective computing

Feasibility of a smartphone application to identify young children at risk for Autism Spectrum Disorder in a low-income community setting in South Africa

Introduction and aims More than 90% of children with Autism Spectrum Disorder (ASD) live in low- and middle-income countries (LMIC) where there is a great need for culturally appropriate, scalable and effective early identification and intervention tools. Smartphone technology and application (‘apps’) may potentially play an important role in this regard. The Autism&Beyond iPhone App was designed as a potential screening tool for ASD risk in children aged 12-72 months. Here we investigated the technical feasibility and cultural acceptability of a smartphone app to determine risk for ASD in children aged 12-72 months in a naturalistic, low-income South African community setting. Methodology 37 typically-developing African children and their parents/carers were recruited from community centres in Khayelitsha Township, Cape Town, South Africa. We implemented a mixed-methods design, collecting both quantitative and qualitative data from participants in 2 stages. In stage 1, we collected quantitative data. With appropriate ethics and consent, parents completed a short technology questionnaire about their familiarity with and access to smartphones, internet and apps, followed by electronic iPhone-based demographic and ASD-related questionnaires. Next, children were shown 3 short videos of 30s each and a mirror stimulus on a study smartphone. The smartphone front facing (“selfie”) camera recorded video of the child’s facial expressions and head movement. Automated computer algorithms quantified positive emotions and time attending to stimuli. We validated the automatic coding by a) comparing the computer-generated analysis to human coding of facial expressions in a random sample (N=9), and b) comparing automated analysis of the South African data (N=33) with a matched American sample (N=33). In stage 2, a subset of families were invited to participate in focus group discussions to provide qualitative data on accessibility, acceptability, and cultural appropriateness of the app in their local community. Results Most parents (64%) owned a smartphone of which all (100%) were Android based, and many used Apps (45%). Human-automated coding showed excellent correlation for positive emotion (ICC= 0.95, 95% CI 0.81-0.99) and no statistically significant differences were observed between the South African and American sample in % time attending to the video stimuli. South African children, however, smiled less at the Toys&Rhymes (SA mean (SD) = 14% (24); USA mean (SD) = 31% (34); p=0.05) and Bunny video (SA mean (SD) = 12% (17); USA mean (SD) = 30% (0.27); p=0.006). Analysis of focus group data indicated that parents/carers found the App relatively easy to use, and would recommend it to others in their community provided the App and data transfer were free. Conclusion The results from this pilot study suggested the App to be technically accurate, accessible and culturally acceptable to families from a low-resource environment in South Africa. Given the differences in positive emotional response between the groups, careful consideration should be given to identify suitable stimuli if % time smiling is to be used as a global marker for autism risk across cultures and environments

12th Annual Focus on Creative Inquiry Poster Forum Program

The 2017 Focus on Creative Inquiry Poster Forum displays a selection of the projects accomplished by Clemson University students in their Creative Inquiry teams. What is Creative Inquiry? It is small-group learning for all students, in all disciplines. It is the imaginative combination of engaged learning and undergraduate research – and it is unique to Clemson University. In Creative Inquiry, small teams of undergraduate students work with faculty mentors to take on problems that spring from their own curiosity, a professor’s challenge, or the pressing needs of the world around them. Students take ownership of their projects. They ask questions, they take risks, and they get answers

Application of Supervised Machine Learning for Behavioral Biomarkers of Autism Spectrum Disorder Based on Electrodermal Activity and Virtual Reality

[EN] Objective: Sensory processing is the ability to capture, elaborate, and integrate information through the five senses and is impaired in over 90% of children with autism spectrum disorder (ASD). The ASD population shows hyper¿hypo sensitiveness to sensory stimuli that can generate alteration in information processing, affecting cognitive and social responses to daily life situations. Structured and semi-structured interviews are generally used for ASD assessment, and the evaluation relies on the examiner¿s subjectivity and expertise, which can lead to misleading outcomes. Recently, there has been a growing need for more objective, reliable, and valid diagnostic measures, such as biomarkers, to distinguish typical from atypical functioning and to reliably track the progression of the illness, helping to diagnose ASD. Implicit measures and ecological valid settings have been showing high accuracy on predicting outcomes and correctly classifying populations in categories. Methods: Two experiments investigated whether sensory processing can discriminate between ASD and typical development (TD) populations using electrodermal activity (EDA) in two multimodal virtual environments (VE): forest VE and city VE. In the first experiment, 24 children with ASD diagnosis and 30 TDs participated in both virtual experiences, and changes in EDA have been recorded before and during the presentation of visual, auditive, and olfactive stimuli. In the second experiment, 40 children have been added to test the model of experiment 1. Results: The first exploratory results on EDA comparison models showed that the integration of visual, auditive, and olfactive stimuli in the forest environment provided higher accuracy (90.3%) on sensory dysfunction discrimination than specific stimuli. In the second experiment, 92 subjects experienced the forest VE, and results on 72 subjects showed that stimuli integration achieved an accuracy of 83.33%. The final confirmatory test set (n = 20) achieved 85% accuracy, simulating a real application of the models. Further relevant result concerns the visual stimuli condition in the first experiment, which achieved 84.6% of accuracy in recognizing ASD sensory dysfunction. Conclusion: According to our studies¿ results, implicit measures, such as EDA, and ecological valid settings can represent valid quantitative methods, along with traditional assessment measures, to classify ASD population, enhancing knowledge on the development of relevant specific treatments.This work was supported by the Spanish Ministry of Economy, Industry, and Competitiveness-funded project Immersive Virtual Environment for the Evaluation and Training of Children with Autism Spectrum Disorder: T Room (IDI-20170912) and by the Generalitat Valenciana-funded project REBRAND (PROMETEU/2019/105).Alcañiz Raya, ML.; Chicchi-Giglioli, IA.; Marín-Morales, J.; Higuera-Trujillo, JL.; Olmos-Raya, E.; Minissi, ME.; Teruel García, G.... (2020). Application of Supervised Machine Learning for Behavioral Biomarkers of Autism Spectrum Disorder Based on Electrodermal Activity and Virtual Reality. Frontiers in Human Neuroscience. 14:1-16. https://doi.org/10.3389/fnhum.2020.00090S11614Allen, R., Davis, R., & Hill, E. (2012). The Effects of Autism and Alexithymia on Physiological and Verbal Responsiveness to Music. Journal of Autism and Developmental Disorders, 43(2), 432-444. doi:10.1007/s10803-012-1587-8Anagnostou, E., Zwaigenbaum, L., Szatmari, P., Fombonne, E., Fernandez, B. A., Woodbury-Smith, M., … Scherer, S. W. (2014). Autism spectrum disorder: advances in evidence-based practice. Canadian Medical Association Journal, 186(7), 509-519. doi:10.1503/cmaj.121756Ashwin, C., Chapman, E., Howells, J., Rhydderch, D., Walker, I., & Baron-Cohen, S. (2014). Enhanced olfactory sensitivity in autism spectrum conditions. Molecular Autism, 5(1), 53. doi:10.1186/2040-2392-5-53Baron-Cohen, S. (1990). Autism: A Specific Cognitive Disorder of & lsquo;Mind-Blindness’. International Review of Psychiatry, 2(1), 81-90. doi:10.3109/09540269009028274Baron-Cohen, S., Ashwin, E., Ashwin, C., Tavassoli, T., & Chakrabarti, B. (2009). Talent in autism: hyper-systemizing, hyper-attention to detail and sensory hypersensitivity. Philosophical Transactions of the Royal Society B: Biological Sciences, 364(1522), 1377-1383. doi:10.1098/rstb.2008.0337Barry, R. J., & James, A. L. (1988). Coding of stimulus parameters in autistic, retarded, and normal children: evidence for a two-factor theory of autism. International Journal of Psychophysiology, 6(2), 139-149. doi:10.1016/0167-8760(88)90045-1Bekele, E., Crittendon, J., Zheng, Z., Swanson, A., Weitlauf, A., Warren, Z., & Sarkar, N. (2014). Assessing the Utility of a Virtual Environment for Enhancing Facial Affect Recognition in Adolescents with Autism. Journal of Autism and Developmental Disorders, 44(7), 1641-1650. doi:10.1007/s10803-014-2035-8Benedek, M., & Kaernbach, C. (2010). A continuous measure of phasic electrodermal activity. Journal of Neuroscience Methods, 190(1), 80-91. doi:10.1016/j.jneumeth.2010.04.028Blascovich, J., Loomis, J., Beall, A. C., Swinth, K. R., Hoyt, C. L., & Bailenson, J. N. (2002). TARGET ARTICLE: Immersive Virtual Environment Technology as a Methodological Tool for Social Psychology. Psychological Inquiry, 13(2), 103-124. doi:10.1207/s15327965pli1302_01Boucsein, W. (2012). Electrodermal Activity. doi:10.1007/978-1-4614-1126-0Brunswik, E. (1955). Representative design and probabilistic theory in a functional psychology. Psychological Review, 62(3), 193-217. doi:10.1037/h0047470BUJNAKOVA, I., ONDREJKA, I., MESTANIK, M., VISNOVCOVA, Z., MESTANIKOVA, A., HRTANEK, I., … TONHAJZEROVA, I. (2016). Autism Spectrum Disorder Is Associated With Autonomic Underarousal. Physiological Research, S673-S682. doi:10.33549/physiolres.933528Chang, C.-C., & Lin, C.-J. (2011). LIBSVM. ACM Transactions on Intelligent Systems and Technology, 2(3), 1-27. doi:10.1145/1961189.1961199Chang, M. C., Parham, L. D., Blanche, E. I., Schell, A., Chou, C.-P., Dawson, M., & Clark, F. (2012). Autonomic and Behavioral Responses of Children With Autism to Auditory Stimuli. American Journal of Occupational Therapy, 66(5), 567-576. doi:10.5014/ajot.2012.004242CHAYTOR, N., SCHMITTEREDGECOMBE, M., & BURR, R. (2006). Improving the ecological validity of executive functioning assessment. Archives of Clinical Neuropsychology, 21(3), 217-227. doi:10.1016/j.acn.2005.12.002Chen, C. P., Keown, C. L., Jahedi, A., Nair, A., Pflieger, M. E., Bailey, B. A., & Müller, R.-A. (2015). Diagnostic classification of intrinsic functional connectivity highlights somatosensory, default mode, and visual regions in autism. NeuroImage: Clinical, 8, 238-245. doi:10.1016/j.nicl.2015.04.002Chita-Tegmark, M. (2016). Attention Allocation in ASD: a Review and Meta-analysis of Eye-Tracking Studies. Review Journal of Autism and Developmental Disorders, 3(3), 209-223. doi:10.1007/s40489-016-0077-xFenwick, T. (2014). Social Media and Medical Professionalism. Academic Medicine, 89(10), 1331-1334. doi:10.1097/acm.0000000000000436Delobel-Ayoub, M., Ehlinger, V., Klapouszczak, D., Maffre, T., Raynaud, J.-P., Delpierre, C., & Arnaud, C. (2015). Socioeconomic Disparities and Prevalence of Autism Spectrum Disorders and Intellectual Disability. PLOS ONE, 10(11), e0141964. doi:10.1371/journal.pone.0141964Di Martino, A., Yan, C.-G., Li, Q., Denio, E., Castellanos, F. X., Alaerts, K., … Milham, M. P. (2013). The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Molecular Psychiatry, 19(6), 659-667. doi:10.1038/mp.2013.78Dudova, I., Vodicka, J., Havlovicova, M., Sedlacek, Z., Urbanek, T., & Hrdlicka, M. (2011). Odor detection threshold, but not odor identification, is impaired in children with autism. European Child & Adolescent Psychiatry, 20(7), 333-340. doi:10.1007/s00787-011-0177-1Fagius, J., & Wallin, B. G. (1980). Sympathetic reflex latencies and conduction velocities in normal man. Journal of the Neurological Sciences, 47(3), 433-448. doi:10.1016/0022-510x(80)90098-2Fenning, R. M., Baker, J. K., Baucom, B. R., Erath, S. A., Howland, M. A., & Moffitt, J. (2017). Electrodermal Variability and Symptom Severity in Children with Autism Spectrum Disorder. Journal of Autism and Developmental Disorders, 47(4), 1062-1072. doi:10.1007/s10803-016-3021-0Forscher, P. S., Lai, C. K., Axt, J. R., Ebersole, C. R., Herman, M., Devine, P. G., & Nosek, B. A. (2019). A meta-analysis of procedures to change implicit measures. Journal of Personality and Social Psychology, 117(3), 522-559. doi:10.1037/pspa0000160Francis, K. (2007). Autism interventions: a critical update. Developmental Medicine & Child Neurology, 47(7), 493-499. doi:10.1111/j.1469-8749.2005.tb01178.xFriston, K. J., Stephan, K. E., Montague, R., & Dolan, R. J. (2014). Computational psychiatry: the brain as a phantastic organ. The Lancet Psychiatry, 1(2), 148-158. doi:10.1016/s2215-0366(14)70275-5Gillberg, C., & Rasmussen, P. (1994). Brief report: Four case histories and a literature review of williams syndrome and autistic behavior. Journal of Autism and Developmental Disorders, 24(3), 381-393. doi:10.1007/bf02172235Hirstein, W., Iversen, P., & Ramachandran, V. S. (2001). Autonomic responses of autistic children to people and objects. Proceedings of the Royal Society of London. Series B: Biological Sciences, 268(1479), 1883-1888. doi:10.1098/rspb.2001.1724Hubert, B. E., Wicker, B., Monfardini, E., & Deruelle, C. (2009). Electrodermal reactivity to emotion processing in adults with autistic spectrum disorders. Autism, 13(1), 9-19. doi:10.1177/1362361308091649Hyde, K. K., Novack, M. N., LaHaye, N., Parlett-Pelleriti, C., Anden, R., Dixon, D. R., & Linstead, E. (2019). Applications of Supervised Machine Learning in Autism Spectrum Disorder Research: a Review. Review Journal of Autism and Developmental Disorders, 6(2), 128-146. doi:10.1007/s40489-019-00158-xJOSEPH, R. M., EHRMAN, K., MCNALLY, R., & KEEHN, B. (2008). Affective response to eye contact and face recognition ability in children with ASD. Journal of the International Neuropsychological Society, 14(6), 947-955. doi:10.1017/s1355617708081344Kandalaft, M. R., Didehbani, N., Krawczyk, D. C., Allen, T. T., & Chapman, S. B. (2012). Virtual Reality Social Cognition Training for Young Adults with High-Functioning Autism. Journal of Autism and Developmental Disorders, 43(1), 34-44. doi:10.1007/s10803-012-1544-6Kreibig, S. D. (2010). Autonomic nervous system activity in emotion: A review. Biological Psychology, 84(3), 394-421. doi:10.1016/j.biopsycho.2010.03.010Kylliäinen, A., & Hietanen, J. K. (2006). Skin Conductance Responses to Another Person’s Gaze in Children with Autism. Journal of Autism and Developmental Disorders, 36(4), 517-525. doi:10.1007/s10803-006-0091-4Kylliäinen, A., Wallace, S., Coutanche, M. N., Leppänen, J. M., Cusack, J., Bailey, A. J., & Hietanen, J. K. (2012). Affective-motivational brain responses to direct gaze in children with autism spectrum disorder. Journal of Child Psychology and Psychiatry, 53(7), 790-797. doi:10.1111/j.1469-7610.2011.02522.xLedoux, K., Coderre, E., Bosley, L., Buz, E., Gangopadhyay, I., & Gordon, B. (2015). The concurrent use of three implicit measures (eye movements, pupillometry, and event-related potentials) to assess receptive vocabulary knowledge in normal adults. Behavior Research Methods, 48(1), 285-305. doi:10.3758/s13428-015-0571-6Leekam, S. R., Nieto, C., Libby, S. J., Wing, L., & Gould, J. (2006). Describing the Sensory Abnormalities of Children and Adults with Autism. Journal of Autism and Developmental Disorders, 37(5), 894-910. doi:10.1007/s10803-006-0218-7Levy, A., & Perry, A. (2011). Outcomes in adolescents and adults with autism: A review of the literature. Research in Autism Spectrum Disorders, 5(4), 1271-1282. doi:10.1016/j.rasd.2011.01.023Li, B., Sharma, A., Meng, J., Purushwalkam, S., & Gowen, E. (2017). Applying machine learning to identify autistic adults using imitation: An exploratory study. PLOS ONE, 12(8), e0182652. doi:10.1371/journal.pone.0182652Liu, W., Li, M., & Yi, L. (2016). Identifying children with autism spectrum disorder based on their face processing abnormality: A machine learning framework. Autism Research, 9(8), 888-898. doi:10.1002/aur.1615Lord, C., Risi, S., DiLavore, P. S., Shulman, C., Thurm, A., & Pickles, A. (2006). Autism From 2 to 9 Years of Age. Archives of General Psychiatry, 63(6), 694. doi:10.1001/archpsyc.63.6.694Lord, C., Rutter, M., & Le Couteur, A. (1994). Autism Diagnostic Interview-Revised: A revised version of a diagnostic interview for caregivers of individuals with possible pervasive developmental disorders. Journal of Autism and Developmental Disorders, 24(5), 659-685. doi:10.1007/bf02172145Loth, E., Spooren, W., Ham, L. M., Isaac, M. B., Auriche-Benichou, C., Banaschewski, T., … Murphy, D. G. M. (2015). Identification and validation of biomarkers for autism spectrum disorders. Nature Reviews Drug Discovery, 15(1), 70-70. doi:10.1038/nrd.2015.7Louwerse, A., van der Geest, J. N., Tulen, J. H. M., van der Ende, J., Van Gool, A. R., Verhulst, F. C., & Greaves-Lord, K. (2013). Effects of eye gaze directions of facial images on looking behaviour and autonomic responses in adolescents with autism spectrum disorders. Research in Autism Spectrum Disorders, 7(9), 1043-1053. doi:10.1016/j.rasd.2013.04.013Lydon, S., Healy, O., Reed, P., Mulhern, T., Hughes, B. M., & Goodwin, M. S. (2014). A systematic review of physiological reactivity to stimuli in autism. Developmental Neurorehabilitation, 19(6), 335-355. doi:10.3109/17518423.2014.971975McCarthy, C., Pradhan, N., Redpath, C., & Adler, A. (2016). Validation of the Empatica E4 wristband. 2016 IEEE EMBS International Student Conference (ISC). doi:10.1109/embsisc.2016.7508621McCormick, C., Hessl, D., Macari, S. L., Ozonoff, S., Green, C., & Rogers, S. J. (2014). Electrodermal and Behavioral Responses of Children With Autism Spectrum Disorders to Sensory and Repetitive Stimuli. Autism Research, 7(4), 468-480. doi:10.1002/aur.1382Miller, L. J., Anzalone, M. E., Lane, S. J., Cermak, S. A., & Osten, E. T. (2007). Concept Evolution in Sensory Integration: A Proposed Nosology for Diagnosis. American Journal of Occupational Therapy, 61(2), 135-140. doi:10.5014/ajot.61.2.135Montague, P. R., Dolan, R. J., Friston, K. J., & Dayan, P. (2012). Computational psychiatry. Trends in Cognitive Sciences, 16(1), 72-80. doi:10.1016/j.tics.2011.11.018Möricke, E., Buitelaar, J. K., & Rommelse, N. N. J. (2015). Do We Need Multiple Informants When Assessing Autistic Traits? The Degree of Report Bias on Offspring, Self, and Spouse Ratings. Journal of Autism and Developmental Disorders, 46(1), 164-175. doi:10.1007/s10803-015-2562-yMurphy, D., & Spooren, W. (2012). EU-AIMS: a boost to autism research. Nature Reviews Drug Discovery, 11(11), 815-816. doi:10.1038/nrd3881Nakai, Y., Takiguchi, T., Matsui, G., Yamaoka, N., & Takada, S. (2017). Detecting Abnormal Word Utterances in Children With Autism Spectrum Disorders. Perceptual and Motor Skills, 124(5), 961-973. doi:10.1177/0031512517716855Nikula, R. (1991). Psychological Correlates of Nonspecific Skin Conductance Responses. Psychophysiology, 28(1), 86-90. doi:10.1111/j.1469-8986.1991.tb03392.xNosek, B. A., Hawkins, C. B., & Frazier, R. S. (2011). Implicit social cognition: from measures to mechanisms. Trends in Cognitive Sciences, 15(4), 152-159. doi:10.1016/j.tics.2011.01.005Palkovitz, R. J., & Wiesenfeld, A. R. (1980). Differential autonomic responses of autistic and normal children. Journal of Autism and Developmental Disorders, 10(3), 347-360. doi:10.1007/bf02408294Parsons, S. (2016). Authenticity in Virtual Reality for assessment and intervention in autism: A conceptual review. Educational Research Review, 19, 138-157. doi:10.1016/j.edurev.2016.08.001Parsons, T. D. (2016). Telemedicine, Mobile, and Internet-Based Neurocognitive Assessment. Clinical Neuropsychology and Technology, 99-111. doi:10.1007/978-3-319-31075-6_6Paulhus, D. L. (1991). Measurement and Control of Response Bias. Measures of Personality and Social Psychological Attitudes, 17-59. doi:10.1016/b978-0-12-590241-0.50006-xPicard, R. W., Fedor, S., & Ayzenberg, Y. (2015). Multiple Arousal Theory and Daily-Life Electrodermal Activity Asymmetry. Emotion Review, 8(1), 62-75. doi:10.1177/1754073914565517Reaven, J. A., Hepburn, S. L., & Ross, R. G. (2008). Use of the ADOS and ADI-R in Children with Psychosis: Importance of Clinical Judgment. Clinical Child Psychology and Psychiatry, 13(1), 81-94. doi:10.1177/1359104507086343Redish, A. D., & Gordon, J. A. (Eds.). (2016). Computational Psychiatry. doi:10.7551/mitpress/9780262035422.001.0001Riby, D. M., Whittle, L., & Doherty-Sneddon, G. (2012). Physiological reactivity to faces via live and video-mediated communication in typical and atypical development. Journal of Clinical and Experimental Neuropsychology, 34(4), 385-395. doi:10.1080/13803395.2011.645019Rogers, S. J., & Ozonoff, S. (2005). Annotation: What do we know about sensory dysfunction in autism? A critical review of the empirical evidence. Journal of Child Psychology and Psychiatry, 46(12), 1255-1268. doi:10.1111/j.1469-7610.2005.01431.xSchmidt, L., Kirchner, J., Strunz, S., Broźus, J., Ritter, K., Roepke, S., & Dziobek, I. (2015). Psychosocial Functioning and Life Satisfaction in Adults With Autism Spectrum Disorder Without Intellectual Impairment. Journal of Clinical Psychology, 71(12), 1259-1268. doi:10.1002/jclp.22225Schoen, S. A. (2009). Physiological and behavioral differences in sensory processing: a comparison of children with Autism Spectrum Disorder and Sensory Processing Disorder. Frontiers in Integrative Neuroscience, 3. doi:10.3389/neuro.07.029.2009Schölkopf, B., Smola, A. J., Williamson, R. C., & Bartlett, P. L. (2000). New Support Vector Algorithms. Neural Computation, 12(5), 1207-1245. doi:10.1162/089976600300015565Slater, M. (2009). Place illusion and plausibility can lead to realistic behaviour in immersive virtual environments. Philosophical Transactions of the Royal Society B: Biological Sciences, 364(1535), 3549-3557. doi:10.1098/rstb.2009.0138Slater, M., & Wilbur, S. (1997). A Framework for Immersive Virtual Environments (FIVE): Speculations on the Role of Presence in Virtual Environments. Presence: Teleoperators and Virtual Environments, 6(6), 603-616. doi:10.1162/pres.1997.6.6.603Stevens, S., & Gruzelier, J. (1984). Electrodermal activity to auditory stimuli in autistic, retarded, and normal children. Journal of Autism and Developmental Disorders, 14(3), 245-260. doi:10.1007/bf02409577Tomchek, S. D., & Dunn, W. (2007). Sensory Processing in Children With and Without Autism: A Comparative Study Using the Short Sensory Profile. American Journal of Occupational Therapy, 61(2), 190-200. doi:10.5014/ajot.61.2.190Tomchek, S. D., Huebner, R. A., & Dunn, W. (2014). Patterns of sensory processing in children with an autism spectrum disorder. Research in Autism Spectrum Disorders, 8(9), 1214-1224. doi:10.1016/j.rasd.2014.06.006Van Engeland, H., Roelofs, J. W., Verbaten, M. N., & Slangen, J. L. (1991). Abnormal electrodermal reactivity to novel visual stimuli in autistic children. Psychiatry Research, 38(1), 27-38. doi:10.1016/0165-1781(91)90050-yVan Hecke, A. V., Stevens, S., Carson, A. M., Karst, J. S., Dolan, B., Schohl, K., … Brockman, S. (2013). Measuring the Plasticity of Social Approach: A Randomized Controlled Trial of the Effects of the PEERS Intervention on EEG Asymmetry in Adolescents with Autism Spectrum Disorders. Journal of Autism and Developmental Disorders, 45(2), 316-335. doi:10.1007/s10803-013-1883-yVolkmar, F. R., State, M., & Klin, A. (2009). Autism and autism spectrum disorders: diagnostic issues for the coming decade. Journal of Child Psychology and Psychiatry, 50(1-2), 108-115. doi:10.1111/j.1469-7610.2008.02010.xWang, X.-J., & Krystal, J. H. (2014). Computational Psychiatry. Neuron, 84(3), 638-654. doi:10.1016/j.neuron.2014.10.018Wang, Y., Hensley, M. K., Tasman, A., Sears, L., Casanova, M. F., & Sokhadze, E. M. (2015). Heart Rate Variability and Skin Conductance During Repetitive TMS Course in Children with Autism. Applied Psychophysiology and Biofeedback, 41(1), 47-60. doi:10.1007/s10484-015-9311-zWhite, M. P., Yeo, N., Vassiljev, P., Lundstedt, R., Wallergård, M., Albin, M., & Lõhmus, M. (2018). A prescription for “nature” – the potential of using virtual nature in therapeutics. Neuropsychiatric Disease and Treatment, Volume 14, 3001-3013. doi:10.2147/ndt.s179038White, S. W., Mazefsky, C. A., Dichter, G. S., Chiu, P. H., Richey, J. A., & Ollendick, T. H. (2014). Social‐cognitive, physiological, and neural mechanisms underlying emotion regulation impairments: understanding anxiety in autism spectrum disorder. International Journal of Developmental Neuroscience, 39(1), 22-36. doi:10.1016/j.ijdevneu.2014.05.012Wing, L., Gould, J., & Gillberg, C. (2011). Autism spectrum disorders in the DSM-V: Better or worse than the DSM-IV? Research in Developmental Disabilities, 32(2), 768-773. doi:10.1016/j.ridd.2010.11.003Yan, K., & Zhang, D. (2015). Feature selection and analysis on correlated gas sensor data with recursive feature elimination. Sensors and Actuators B: Chemical, 212, 353-363. doi:10.1016/j.snb.2015.02.025Zahn, T. P., Rumsey, J. M., & Van Kammen, D. P. (1987). Autonomic nervous system activity in autistic, schizophrenic, and normal men: Effects of stimulus significance. Journal of Abnormal Psychology, 96(2), 135-144. doi:10.1037/0021-843x.96.2.13

Proprioceptive and Kinematic Profiles for Customized Human‐ Robot Interaction for People Suffering from Autism

In this chapter, we presented a method to define individual profiles in order to develop a new personalized robot‐based social interaction for individual with autistic spectrum disorder (ASD) with the hypothesis that hyporeactivity to visual motion and an overreliance on proprioceptive information would be linked to difficulties in integrating social cues and in engaging in successful interactions. We succeed to form three groups among our 19 participants (children, teenagers, and adults with ASD), describing each participant\u27s response to visual and proprioceptive inputs. We conducted a first experiment to present the robot Nao as a social companion and to avoid fear or stress toward the robot in future experiment. No direct link between the behavior of the participants toward the robot and their proprioceptive and visual profiles was observed. Still, we found encouraging results going in the direction of our hypothesis. In addition, almost all of our participants showed great interest to Nao. Defining such individual profiles prior to social interactions with a robot could provide promising strategies for designing successful and adapted human‐robot interaction (HRI) for individuals with ASD