Predictive text entry in immersive environments

One of the classic problems with immersive environments is data entry; with a head-mounted display (HMD) the user can no longer see the keyboard. Although for many applications data entry is not a requirement, for some it is essential: communicating in collaborative environments, entering a filename to which work can be saved, or accessing system controls. Combining data gloves and a graphically represented keyboard with a predictive spelling paradigm, we describe an effective text entry technique for immersive environments; we explore the key issues when using such a technique, and report the results of preliminary usability testing

Body-Borne Computers as Extensions of Self

The opportunities for wearable technologies go well beyond always-available information displays or health sensing devices. The concept of the cyborg introduced by Clynes and Kline, along with works in various fields of research and the arts, offers a vision of what technology integrated with the body can offer. This paper identifies different categories of research aimed at augmenting humans. The paper specifically focuses on three areas of augmentation of the human body and its sensorimotor capabilities: physical morphology, skin display, and somatosensory extension. We discuss how such digital extensions relate to the malleable nature of our self-image. We argue that body-borne devices are no longer simply functional apparatus, but offer a direct interplay with the mind. Finally, we also showcase some of our own projects in this area and shed light on future challenges

Digital 3D Technologies for Humanities Research and Education: An Overview

Digital 3D modelling and visualization technologies have been widely applied to support research in the humanities since the 1980s. Since technological backgrounds, project opportunities, and methodological considerations for application are widely discussed in the literature, one of the next tasks is to validate these techniques within a wider scientific community and establish them in the culture of academic disciplines. This article resulted from a postdoctoral thesis and is intended to provide a comprehensive overview on the use of digital 3D technologies in the humanities with regards to (1) scenarios, user communities, and epistemic challenges; (2) technologies, UX design, and workflows; and (3) framework conditions as legislation, infrastructures, and teaching programs. Although the results are of relevance for 3D modelling in all humanities disciplines, the focus of our studies is on modelling of past architectural and cultural landscape objects via interpretative 3D reconstruction methods

3D printing and immersive visualization for improved perception of ancient artifacts

This article investigates the use of 3D immersive virtual environments and 3D prints for interaction with past material culture over traditional observation without manipulation. Our work is motivated by studies in heritage, museum, and cognitive sciences indicating the importance of object manipulation for understanding present and ancient artifacts. While virtual immersive environments and 3D prints have started to be incorporated in heritage research and museum displays as a way to provide improved manipulation experiences, little is known about how these new technologies affect the perception of our past. This article provides first results obtained with three experiments designed to investigate the benefits and tradeoffs in using these technologies. Our results indicate that traditional museum displays limit the experience with past material culture, and reveal how our sample of participants favor tactile and immersive 3D virtual experiences with artifacts over visual non-manipulative experiences with authentic objects. This paper is part of a larger study on how people perceive ancient artifacts, which was partially funded by the University of California Humanities Network and the Center for the Humanities at the University of California, Merced.This is the author accepted manuscript. The final version is available from MIT Press via http://dx.doi.org/10.1162/PRES_a_0022

Interfacce uomo-macchina nella realtà virtuale

Questo capitolo fornisce una descrizione dei principali elementi che influenzano l'interazione uomo-macchina in riferimento alla realtà virtuale, per come si configurano attualmente, e per come si prevede si svilupperanno in un prossimo futuro. Il capitolo è organizzato nel modo seguente: la sezione 1.1 presenta il concetto di realtà virtuale soprattutto in relazione alle possibilità offerte per quanto riguarda l’interazione tra uomo e macchina, ed alle applicazioni di nuova generazione. La sezione successiva descrive i principali requisiti ed i vincoli che un sistema di realtà virtuale deve soddisfare per riuscire a fornire all’utente un’impressione convincente e delle esperienze realmente immersive. La sezione 1.3 si concentra sul feedback sensoriale principale, descrivendo le principali tecnologie di nuova generazione per la realizzazione di dispositivi in grado di fornire delle sensazioni visive e tattili estremamente realistiche. Infine la sezione 1.4 descrive brevemente alcuni esempi di applicazioni di realtà virtuale realizzate dagli autori, nel campo della simulazione chirurgica, dei musei virtuali e dei sistemi di visualizzazione autostereoscopici multiutente, e la sezione 1.5 discute brevemente la situazione attuale ed il potenziale futuro della disciplina.289-33

Framing the past: How virtual experience affects bodily description of artefacts

This study uses a novel, interdisciplinary approach to investigate how people describe ancient artefacts. Here, we focus on gestures. Researchers have shown that gestures are important in communication, and those researchers often make a distinction between beat and iconic gestures. Iconic gestures convey meaning, specifically, visual-spatial information. Beat gestures do not convey meaning; they facilitate lexical access. In our study, we videotaped participants while they described artefacts presented through varied media: visual examination, physical interaction, and three-dimensional virtual and material replica (i.e., 3D prints) interaction. Video analysis revealed that media type affected gesture production. Participants who viewed actual objects displayed in a museum-style case produced few gestures in their descriptions. This finding suggests that traditional museum displays may diminish or limit museum users degree of engagement with ancient artefacts. This interdisciplinary work advances our knowledge of material culture by providing new insights into how people use and experience ancient artefacts in varied presentations. Implications for virtual reproduction in research, education, and communication in archaeology are discussed.This paper is part of a larger study on how people perceive ancient artefacts, which was partially funded by the University of California Humanities Network and the Center for the Humanities at the University of California, Merced.This is the author accepted manuscript. The final version is available from Elsevier via http://dx.doi.org/10.1016/j.culher.2015.04.00

Machine Learning and Virtual Reality on Body Movements¿ Behaviors to Classify Children with Autism Spectrum Disorder

[EN] Autism spectrum disorder (ASD) is mostly diagnosed according to behavioral symptoms in sensory, social, and motor domains. Improper motor functioning, during diagnosis, involves the qualitative evaluation of stereotyped and repetitive behaviors, while quantitative methods that classify body movements' frequencies of children with ASD are less addressed. Recent advances in neuroscience, technology, and data analysis techniques are improving the quantitative and ecological validity methods to measure specific functioning in ASD children. On one side, cutting-edge technologies, such as cameras, sensors, and virtual reality can accurately detect and classify behavioral biomarkers, as body movements in real-life simulations. On the other, machine-learning techniques are showing the potential for identifying and classifying patients' subgroups. Starting from these premises, three real-simulated imitation tasks have been implemented in a virtual reality system whose aim is to investigate if machine-learning methods on movement features and frequency could be useful in discriminating ASD children from children with typical neurodevelopment. In this experiment, 24 children with ASD and 25 children with typical neurodevelopment participated in a multimodal virtual reality experience, and changes in their body movements were tracked by a depth sensor camera during the presentation of visual, auditive, and olfactive stimuli. The main results showed that ASD children presented larger body movements than TD children, and that head, trunk, and feet represent the maximum classification with an accuracy of 82.98%. Regarding stimuli, visual condition showed the highest accuracy (89.36%), followed by the visual-auditive stimuli (74.47%), and visual-auditive-olfactory stimuli (70.21%). Finally, the head showed the most consistent performance along with the stimuli, from 80.85% in visual to 89.36% in visual-auditive-olfactory condition. The findings showed the feasibility of applying machine learning and virtual reality to identify body movements' biomarkers that could contribute to improving ASD diagnosis.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 (PROMETEO/2019/105). Furthermore, this work was co-founded by the European Union through the Operational Program of the European Regional development Fund (ERDF) of the Valencian Community 2014-2020 (IDIFEDER/2018/029).Alcañiz Raya, ML.; Marín-Morales, J.; Minissi, ME.; Teruel Garcia, G.; Abad, L.; Chicchi-Giglioli, IA. (2020). Machine Learning and Virtual Reality on Body Movements¿ Behaviors to Classify Children with Autism Spectrum Disorder. Journal of Clinical Medicine. 9(5):1-20. https://doi.org/10.3390/jcm9051260S12095https://www.who.int/news-room/fact-sheets/detail/autism-spectrum-disordersAnagnostou, 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.121756Lord, 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.63.6.694Schmidt, 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.22225Turner, M. (1999). Annotation: Repetitive Behaviour in Autism: A Review of Psychological Research. Journal of Child Psychology and Psychiatry, 40(6), 839-849. doi:10.1111/1469-7610.00502Lewis, M. H., & Bodfish, J. W. (1998). Repetitive behavior disorders in autism. Mental Retardation and Developmental Disabilities Research Reviews, 4(2), 80-89. doi:10.1002/(sici)1098-2779(1998)4:23.0.co;2-0Mahone, E. M., Bridges, D., Prahme, C., & Singer, H. S. (2004). Repetitive arm and hand movements (complex motor stereotypies) in children. The Journal of Pediatrics, 145(3), 391-395. doi:10.1016/j.jpeds.2004.06.014MacDonald, R., Green, G., Mansfield, R., Geckeler, A., Gardenier, N., Anderson, J., … Sanchez, J. (2007). Stereotypy in young children with autism and typically developing children. Research in Developmental Disabilities, 28(3), 266-277. doi:10.1016/j.ridd.2006.01.004Singer, H. S. (2009). Motor Stereotypies. Seminars in Pediatric Neurology, 16(2), 77-81. doi:10.1016/j.spen.2009.03.008Lidstone, J., Uljarević, M., Sullivan, J., Rodgers, J., McConachie, H., Freeston, M., … Leekam, S. (2014). Relations among restricted and repetitive behaviors, anxiety and sensory features in children with autism spectrum disorders. Research in Autism Spectrum Disorders, 8(2), 82-92. doi:10.1016/j.rasd.2013.10.001GOLDMAN, S., WANG, C., SALGADO, M. W., GREENE, P. E., KIM, M., & RAPIN, I. (2009). Motor stereotypies in children with autism and other developmental disorders. Developmental Medicine & Child Neurology, 51(1), 30-38. doi:10.1111/j.1469-8749.2008.03178.xLord, 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/bf02172145Volkmar, 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.xReaven, 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:10.1177/1359104507086343Torres, E. B., Brincker, M., Isenhower, R. W., Yanovich, P., Stigler, K. A., Nurnberger, J. I., … José, J. V. (2013). Autism: the micro-movement perspective. Frontiers in Integrative Neuroscience, 7. doi:10.3389/fnint.2013.00032Möricke, 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-yCHAYTOR, 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.2005.12.002Brunswik, E. (1955). Representative design and probabilistic theory in a functional psychology. Psychological Review, 62(3), 193-217. doi:10.1037/h0047470Gillberg, 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/bf02172235Parsons, 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.2016.08.001Francis, K. (2005). Autism interventions: a critical update. Developmental Medicine & Child Neurology, 47(7), 493-499. doi:10.1017/s0012162205000952Albinali, F., Goodwin, M. S., & Intille, S. S. (2009). Recognizing stereotypical motor movements in the laboratory and classroom. Proceedings of the 11th international conference on Ubiquitous computing. doi:10.1145/1620545.1620555Pyles, D. A. M., Riordan, M. M., & Bailey, J. S. (1997). The stereotypy analysis: An instrument for examining environmental variables associated with differential rates of stereotypic behavior. Research in Developmental Disabilities, 18(1), 11-38. doi:10.1016/s0891-4222(96)00034-0Nosek, 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.2011.01.005Forscher, 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/pspa0000160LeDoux, J. E., & Pine, D. S. (2016). Using Neuroscience to Help Understand Fear and Anxiety: A Two-System Framework. American Journal of Psychiatry, 173(11), 1083-1093. doi:10.1176/appi.ajp.2016.16030353Fenning, 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/s10803-016-3021-0Nikula, R. (1991). Psychological Correlates of Nonspecific Skin Conductance Responses. Psychophysiology, 28(1), 86-90. doi:10.1111/j.1469-8986.1991.tb03392.xAlcañiz Raya, M., Chicchi Giglioli, I. A., Marín-Morales, J., Higuera-Trujillo, J. L., Olmos, E., Minissi, M. E., … Abad, L. (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. doi:10.3389/fnhum.2020.00090Cunningham, W. A., Raye, C. L., & Johnson, M. K. (2004). Implicit and Explicit Evaluation: fMRI Correlates of Valence, Emotional Intensity, and Control in the Processing of Attitudes. Journal of Cognitive Neuroscience, 16(10), 1717-1729. doi:10.1162/0898929042947919Kopton, I. M., & Kenning, P. (2014). Near-infrared spectroscopy (NIRS) as a new tool for neuroeconomic research. Frontiers in Human Neuroscience, 8. doi:10.3389/fnhum.2014.00549Nickel, P., & Nachreiner, F. (2003). Sensitivity and Diagnosticity of the 0.1-Hz Component of Heart Rate Variability as an Indicator of Mental Workload. Human Factors: The Journal of the Human Factors and Ergonomics Society, 45(4), 575-590. doi:10.1518/hfes.45.4.575.27094Di 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.2013.78Van Hecke, A. V., Lebow, J., Bal, E., Lamb, D., Harden, E., Kramer, A., … Porges, S. W. (2009). Electroencephalogram and Heart Rate Regulation to Familiar and Unfamiliar People in Children With Autism Spectrum Disorders. Child Development, 80(4), 1118-1133. doi:10.1111/j.1467-8624.2009.01320.xCoronato, A., De Pietro, G., & Paragliola, G. (2014). A situation-aware system for the detection of motion disorders of patients with Autism Spectrum Disorders. Expert Systems with Applications, 41(17), 7868-7877. doi:10.1016/j.eswa.2014.05.011Goodwin, M. S., Intille, S. S., Albinali, F., & Velicer, W. F. (2010). Automated Detection of Stereotypical Motor Movements. Journal of Autism and Developmental Disorders, 41(6), 770-782. doi:10.1007/s10803-010-1102-zRodrigues, J. L., Gonçalves, N., Costa, S., & Soares, F. (2013). Stereotyped movement recognition in children with ASD. Sensors and Actuators A: Physical, 202, 162-169. doi:10.1016/j.sna.2013.04.019Crippa, 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. doi:10.1007/s10803-015-2379-8Wedyan, M., Al-Jumaily, A., & Crippa, A. (2019). Using machine learning to perform early diagnosis of Autism Spectrum Disorder based on simple upper limb movements. International Journal of Hybrid Intelligent Systems, 15(4), 195-206. doi:10.3233/his-190278Parsons, S., Mitchell, P., & Leonard, A. (2004). The Use and Understanding of Virtual Environments by Adolescents with Autistic Spectrum Disorders. Journal of Autism and Developmental Disorders, 34(4), 449-466. doi:10.1023/b:jadd.0000037421.98517.8dParsons, T. D., Rizzo, A. A., Rogers, S., & York, P. (2009). Virtual reality in paediatric rehabilitation: A review. Developmental Neurorehabilitation, 12(4), 224-238. doi:10.1080/17518420902991719Bowman, D. A., Gabbard, J. L., & Hix, D. (2002). A Survey of Usability Evaluation in Virtual Environments: Classification and Comparison of Methods. Presence: Teleoperators and Virtual Environments, 11(4), 404-424. doi:10.1162/105474602760204309Pastorelli, E., & Herrmann, H. (2013). A Small-scale, Low-budget Semi-immersive Virtual Environment for Scientific Visualization and Research. Procedia Computer Science, 25, 14-22. doi:10.1016/j.procs.2013.11.003Cobb, S. V. G., Nichols, S., Ramsey, A., & Wilson, J. R. (1999). Virtual Reality-Induced Symptoms and Effects (VRISE). Presence: Teleoperators and Virtual Environments, 8(2), 169-186. doi:10.1162/105474699566152Wallace, S., Parsons, S., Westbury, A., White, K., White, K., & Bailey, A. (2010). Sense of presence and atypical social judgments in immersive virtual environments. Autism, 14(3), 199-213. doi:10.1177/1362361310363283Lorenzo, G., Lledó, A., Arráez-Vera, G., & Lorenzo-Lledó, A. (2018). The application of immersive virtual reality for students with ASD: A review between 1990–2017. Education and Information Technologies, 24(1), 127-151. doi:10.1007/s10639-018-9766-7Bailenson, J. N., Yee, N., Merget, D., & Schroeder, R. (2006). The Effect of Behavioral Realism and Form Realism of Real-Time Avatar Faces on Verbal Disclosure, Nonverbal Disclosure, Emotion Recognition, and Copresence in Dyadic Interaction. Presence: Teleoperators and Virtual Environments, 15(4), 359-372. doi:10.1162/pres.15.4.359Cipresso, P., Giglioli, I. A. C., Raya, M. A., & Riva, G. (2018). The Past, Present, and Future of Virtual and Augmented Reality Research: A Network and Cluster Analysis of the Literature. Frontiers in Psychology, 9. doi:10.3389/fpsyg.2018.02086Cummings, J. J., & Bailenson, J. N. (2015). How Immersive Is Enough? A Meta-Analysis of the Effect of Immersive Technology on User Presence. Media Psychology, 19(2), 272-309. doi:10.1080/15213269.2015.1015740Skalski, P., & Tamborini, R. (2007). The Role of Social Presence in Interactive Agent-Based Persuasion. Media Psychology, 10(3), 385-413. doi:10.1080/15213260701533102Slater, 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.2009.0138Baños, R. M., Botella, C., Garcia-Palacios, A., Villa, H., Perpiña, C., & Alcañiz, M. (2000). Presence and Reality Judgment in Virtual Environments: A Unitary Construct? CyberPsychology & Behavior, 3(3), 327-335. doi:10.1089/10949310050078760Bente, G., Rüggenberg, S., Krämer, N. C., & Eschenburg, F. (2008). Avatar-Mediated Networking: Increasing Social Presence and Interpersonal Trust in Net-Based Collaborations. Human Communication Research, 34(2), 287-318. doi:10.1111/j.1468-2958.2008.00322.xHeeter, C. (1992). Being There: The Subjective Experience of Presence. Presence: Teleoperators and Virtual Environments, 1(2), 262-271. doi:10.1162/pres.1992.1.2.262Sanchez-Vives, M. V., & Slater, M. (2005). From presence to consciousness through virtual reality. Nature Reviews Neuroscience, 6(4), 332-339. doi:10.1038/nrn1651Mohr, D. C., Burns, M. N., Schueller, S. M., Clarke, G., & Klinkman, M. (2013). Behavioral Intervention Technologies: Evidence review and recommendations for future research in mental health. General Hospital Psychiatry, 35(4), 332-338. doi:10.1016/j.genhosppsych.2013.03.008Neguț, A., Matu, S.-A., Sava, F. A., & David, D. (2016). Virtual reality measures in neuropsychological assessment: a meta-analytic review. The Clinical Neuropsychologist, 30(2), 165-184. doi:10.1080/13854046.2016.1144793Riva, G. (2005). Virtual Reality in Psychotherapy: Review. CyberPsychology & Behavior, 8(3), 220-230. doi:10.1089/cpb.2005.8.220Valmaggia, L. R., Latif, L., Kempton, M. J., & Rus-Calafell, M. (2016). Virtual reality in the psychological treatment for mental health problems: An systematic review of recent evidence. Psychiatry Research, 236, 189-195. doi:10.1016/j.psychres.2016.01.015Mesa-Gresa, P., Gil-Gómez, H., Lozano-Quilis, J.-A., & Gil-Gómez, J.-A. (2018). Effectiveness of Virtual Reality for Children and Adolescents with Autism Spectrum Disorder: An Evidence-Based Systematic Review. Sensors, 18(8), 2486. doi:10.3390/s18082486Cheng, Y., & Ye, J. (2010). Exploring the social competence of students with autism spectrum conditions in a collaborative virtual learning environment – The pilot study. Computers & Education, 54(4), 1068-1077. doi:10.1016/j.compedu.2009.10.011Jarrold, W., Mundy, P., Gwaltney, M., Bailenson, J., Hatt, N., McIntyre, N., … Swain, L. (2013). Social Attention in a Virtual Public Speaking Task in Higher Functioning Children With Autism. Autism Research, 6(5), 393-410. doi:10.1002/aur.1302Forgeot d’Arc, B., Ramus, F., Lefebvre, A., Brottier, D., Zalla, T., Moukawane, S., … Delorme, R. (2014). Atypical Social Judgment and Sensitivity to Perceptual Cues in Autism Spectrum Disorders. Journal of Autism and Developmental Disorders, 46(5), 1574-1581. doi:10.1007/s10803-014-2208-5Maskey, M., Lowry, J., Rodgers, J., McConachie, H., & Parr, J. R. (2014). Reducing Specific Phobia/Fear in Young People with Autism Spectrum Disorders (ASDs) through a Virtual Reality Environment Intervention. PLoS ONE, 9(7), e100374. doi:10.1371/journal.pone.0100374Baron-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.2008.0337Tomchek, 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.2014.06.006Boyd, B. A., Baranek, G. T., Sideris, J., Poe, M. D., Watson, L. R., Patten, E., & Miller, H. (2010). Sensory features and repetitive behaviors in children with autism and developmental delays. Autism Research, n/a-n/a. doi:10.1002/aur.124Gabriels, R. L., Agnew, J. A., Miller, L. J., Gralla, J., Pan, Z., Goldson, E., … Hooks, E. (2008). Is there a relationship between restricted, repetitive, stereotyped behaviors and interests and abnormal sensory response in children with autism spectrum disorders? Research in Autism Spectrum Disorders, 2(4), 660-670. doi:10.1016/j.rasd.2008.02.002Cao, Z., Hidalgo, G., Simon, T., Wei, S.-E., & Sheikh, Y. (2021). OpenPose: Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fields. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(1), 172-186. doi:10.1109/tpami.2019.2929257Schölkopf, B., Smola, A. J., Williamson, R. C., & Bartlett, P. L. (2000). New Support Vector Algorithms. Neural Computation, 12(5), 1207-1245. doi:10.1162/089976600300015565Yan, 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.2015.02.025Chang, C.-C., & Lin, C.-J. (2011). LIBSVM. ACM Transactions on Intelligent Systems and Technology, 2(3), 1-27. doi:10.1145/1961189.1961199O’Neill, M., & Jones, R. S. P. (1997). Journal of Autism and Developmental Disorders, 27(3), 283-293. doi:10.1023/a:1025850431170Foss-Feig, J. H., Kwakye, L. D., Cascio, C. J., Burnette, C. P., Kadivar, H., Stone, W. L., & Wallace, M. T. (2010). An extended multisensory temporal binding window in autism spectrum disorders. Experimental Brain Research, 203(2), 381-389. doi:10.1007/s00221-010-2240-4Courchesne, E., Lincoln, A. J., Kilman, B. A., & Galambos, R. (1985). Event-related brain potential correlates of the processing of novel visual and auditory information in autism. Journal of Autism and Developmental Disorders, 15(1), 55-76. doi:10.1007/bf01837899Russo, N., Foxe, J. J., Brandwein, A. B., Altschuler, T., Gomes, H., & Molholm, S. (2010). Multisensory processing in children with autism: high-density electrical mapping of auditory-somatosensory integration. Autism Research, 3(5), 253-267. doi:10.1002/aur.152Ament, K., Mejia, A., Buhlman, R., Erklin, S., Caffo, B., Mostofsky, S., & Wodka, E. (2014). Evidence for Specificity of Motor Impairments in Catching and Balance in Children with Autism. Journal of Autism and Developmental Disorders, 45(3), 742-751. doi:10.1007/s10803-014-2229-

Virtual Reality Adaptation Using Electrodermal Activity to Support the User Experience

Virtual reality is increasingly used for tasks such as work and education. Thus, rendering scenarios that do not interfere with such goals and deplete user experience are becoming progressively more relevant. We present a physiologically adaptive system that optimizes the virtual environment based on physiological arousal, i.e., electrodermal activity. We investigated the usability of the adaptive system in a simulated social virtual reality scenario. Participants completed an n-back task (primary) and a visual detection (secondary) task. Here, we adapted the visual complexity of the secondary task in the form of the number of non-player characters of the secondary task to accomplish the primary task. We show that an adaptive virtual reality can improve users' comfort by adapting to physiological arousal regarding the task complexity. Our findings suggest that physiologically adaptive virtual reality systems can improve users' experience in a wide range of scenarios