An approach to promote social and communication behaviors in children with autism spectrum disorders : robot based intervention

Most autistic people present some difficulties in developing social behavior, living in their own world. This study has the goal to improve the social life of children with autism with a main focus in promoting their social interaction and communication. It is necessary to call for children’s attention and enforce their collaboration, where a robot, LEGO MindStorm, behaves as a mediator/promoter of this interaction. A set of experiments designed to share objects and fulfill simple orders, by the 11 years old autistic child at the time of daily routine work and in-game with the robot, are described. The generalization of the acquired skills by the child in new contexts and environments are also tested. Results are described showing the outcomes of the experiments.Fundação para a Ciência e a Tecnologia (FCT) - R&D projecto RIPD/ADA/109407/200

WearCam: A head mounted wireless camera for monitoring gaze attention and for the diagnosis of developmental disorders in young children

Autism covers a large spectrum of disorders that affect the individual’s way of interacting socially and is often revealed by the individual’s lack of interest in gazing at human faces. Currently Autism is diagnosed in children no younger than 2 years old. This paper presents a new monitoring device, the WearCam, to help forming a diagnosis of this neurodevelopmental disorder at an earlier age than currently possible. The WearCam consists of a wireless camera located on the forefront of the child. The WearCam collects videos from the viewpoint of the child’s head. Color detection, face detection and gaze detection are run on the data in order to locate the approximate gaze direction of the child and determine where her attention is drawn to (persons, objects, etc.). We report on early tests of the camera within normally developing children. Firstly the technical characteristics of the current prototype of the WearCam will be described. Afterwards the type of data collected with this device with young children will be shown

A portable audio/video recorder for longitudinal study of child development

Collection and analysis of ultra-dense, longitudinal observational data of child behavior in natural, ecologically valid, non-laboratory settings holds significant promise for advancing the understanding of child development and developmental disorders such as autism. To this end, we created the Speechome Recorder - a portable version of the embedded audio/video recording technology originally developed for the Human Speechome Project - to facilitate swift, cost-effective deployment in home environments. Recording child behavior daily in these settings will enable detailed study of developmental trajectories in children from infancy through early childhood, as well as typical and atypical dynamics of communication and social interaction as they evolve over time. Its portability makes possible potentially large-scale comparative study of developmental milestones in both neurotypical and developmentally delayed children. In brief, the Speechome Recorder was designed to reduce cost, complexity, invasiveness and privacy issues associated with naturalistic, longitudinal recordings of child development.National Institutes of Health (U.S.) (Grant R01 2DC007428)Nancy Lurie Marks Family Foundatio

Investigating Gaze of Children with ASD in Naturalistic Settings.

BACKGROUND: Visual behavior is known to be atypical in Autism Spectrum Disorders (ASD). Monitor-based eye-tracking studies have measured several of these atypicalities in individuals with Autism. While atypical behaviors are known to be accentuated during natural interactions, few studies have been made on gaze behavior in natural interactions. In this study we focused on i) whether the findings done in laboratory settings are also visible in a naturalistic interaction; ii) whether new atypical elements appear when studying visual behavior across the whole field of view. METHODOLOGY/PRINCIPAL FINDINGS: Ten children with ASD and ten typically developing children participated in a dyadic interaction with an experimenter administering items from the Early Social Communication Scale (ESCS). The children wore a novel head-mounted eye-tracker, measuring gaze direction and presence of faces across the child's field of view. The analysis of gaze episodes to faces revealed that children with ASD looked significantly less and for shorter lapses of time at the experimenter. The analysis of gaze patterns across the child's field of view revealed that children with ASD looked downwards and made more extensive use of their lateral field of view when exploring the environment. CONCLUSIONS/SIGNIFICANCE: The data gathered in naturalistic settings confirm findings previously obtained only in monitor-based studies. Moreover, the study allowed to observe a generalized strategy of lateral gaze in children with ASD when they were looking at the objects in their environment

The IMMED Project: Wearable Video Monitoring of People with Age Dementia

International audienceIn this paper, we describe a new application for multimedia indexing, using a system that monitors the instrumental activities of daily living to assess the cognitive decline caused by dementia. The system is composed of a wearable camera device designed to capture audio and video data of the instrumental activities of a patient, which is leveraged with multimedia indexing techniques in order to allow medical specialists to analyze several hour long observation shots efficiently

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-

Socially Assistive Robots for Older Adults and People with Autism: An Overview

Over one billion people in the world suffer from some form of disability. Nevertheless, according to the World Health Organization, people with disabilities are particularly vulnerable to deficiencies in services, such as health care, rehabilitation, support, and assistance. In this sense, recent technological developments can mitigate these deficiencies, offering less-expensive assistive systems to meet users’ needs. This paper reviews and summarizes the research efforts toward the development of these kinds of systems, focusing on two social groups: older adults and children with autism.This research was funded by the Spanish Government TIN2016-76515-R grant for the COMBAHO project, supported with Feder funds. It has also been supported by Spanish grants for PhD studies ACIF/2017/243 and FPU16/00887

Calibration-Free Eye Gaze Direction Detection with Gaussian Processes

In this paper we present a solution for eye gaze detection from a wireless head mounted camera designed for children aged between 6 months and 18 months. Due to the constraints of working with very young children, the system does not seek to be as accurate as other state-of-the-art eye trackers, however it requires no calibration process from the wearer. Gaussian Process Regression and Support Vector Machines are used to analyse the raw pixel data from the video input and return an estimate of the child's gaze direction. A confidence map is used to determine the accuracy the system can expect for each coordinate on the image. The best accuracy so far obtained by the system is 2.34$^{circ}$ on adult subjects, tests with children remain to be done