In this paper, we present a set up of cloudconnected social robots to measure and model the effect of LEGO Engineering and its collaborative nature on the development of social skills in children with Autism Spectrum Disorder (ASD). Here we introduce the first approach to the modelling process designed.Postprint (published version

Albo-Canals, Jordi

Angulo Bahón, Cecilio

Barco, Alex

Heerink, Marcel

Yanez, Carolina

In this paper, we present a set up of cloudconnected social robots to measure and model the effect of LEGO Engineering and its collaborative nature on the development of social skills in children with Autism Spectrum Disorder (ASD). Here we introduce the first approach to the modelling process designed

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Modelling social skills and problem solving strategies used by children with ASD through cloud connected social robots as data loggers: first modelling approach

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Modelling Social Skills and Problem Solving Strategies used byChildren with ASD through Cloud Connected Social Robots as DataLoggers: First Modelling ApproachJordi Albo-Canals1, Carolina Yanez2, Alex Barco3, Cecilio Angulo4and Marcel Heerink5Abstract— In this paper, we present a set up of cloud-connected social robots to measure and model the effectof LEGO Engineering and its collaborative nature on thedevelopment of social skills in children with Autism SpectrumDisorder (ASD). Here we introduce the first approach to themodelling process designed.I. INTRODUCTIONThere exists a growing body of research centered aroundrobotics and autism, using a social robot as a data logger.Previous research includes children with ASD working withhumanoid robots (e.g., NAO or KASPAR), working togetherto build robots [1], [2], talking to the robot and mimickinga robot [3]. Also, we present a cloud-based system to speedup the analysis of how therapies based on working in groupsand building LEGO change their social skills, social network,and cognitive skills.The project consists of an 8-week study (one two-hoursession per week).The sessions have a format of a workshopon building LEGO Robotics with a Robot Companion (NAORobot, AISOY Robot, or SAMSUNG Robot) that will be onthe table as a helper, social mediator, and will remind thekids of the time schedule.During the sessions, Children sit at a table with a laptopto program the LEGO robot and a complete LEGO MIND-STORM EV3 set (The LEGO Robot). Children work ingroups of 2 selected at random, and they keep the samegroup for all sessions. A Social Robot (NAO Robot, AISOYRobot, or SAMSUNG Robot) is on the table as a helper,social mediator, and remember the time schedule.In each of these sessions, we collect information thatallows us to create a reliable model of how these childrensocialise with each other and with the adults in the classroom,and how these children solve engineering problems (seeFigure 1). While the children with ASD social skills modelhas been studied since a long time ago, the engineeringthinking skills is not approached by the community. Previous*LEGO Foundation and TTT Outreach, Think Tank Team, SamsungResearch America1Jordi Albo-Canals is with Tufts Universityjordi.albo canals@tufts.edu2Carolina Yanez is with la Universidad de Chile,cyanez.uchile@gmail.com3Alex Barco is with la Salle, Ramon Llull Universityabarco@salleurl.edu4Cecilio Angulo is with Universitat Polite`cnica de Catalunyacecilio.angulo@upc.edu5Marcel Heerink is with Windesheim Flevoland Universitym.heerink@windesheimflevoland.nlstudies showed that only people in the field of science andtechnology were trained in engineering skills. However, ithas been proved that engineering skills are needed in veryday life, bringing clear benefits to the quality of living forthose children who can acquire and use them [4], [5]. Dochildren with ASD follow the same strategies that neurotyp-ical children? How they are dealing with this problem?.The model obtained should give an answer to these twoquestions and see if we can redesign their educational andtraining system [6]. Furthermore, in [7] is claimed that thereis a connection between engineering thinking and humansensitivity that makes the quality of live better.Fig. 1. Schematic of how data flow through the cloud until the model isobtained.II. MODELLING PROCESSThe modelling process is divided into two paths accordingto the two outcomes mentioned in the introduction of thispaper. On the one hand we model how children with ASDdeal with the social situation, and, on the other hand, we aremodelling how they solve engineering problems (see Figure2).Through the video observation, the quantitative data ob-tained from the interactive systems, and the descriptors ob-tained after processing the information through the machinelearning algorithm we can identify the interactive behaviorsand their quality in terms of intensity and duration.The system is supposed to identify interactive behaviorsand to measure the amount of social engagement childrenare experiencing.Fig. 2. Modelling description processA. Human ExpertsThrough video observation (video coding) and question-naires to the students, parents, and teacher, we are going tocollect qualitative data. Through a web-based tactile interfaceto interact with the robot and the video recordings we aregoing to extract quantitative data. The qualitative data thatwe are going to measure is detailed in the VIDEO CODINGdocument and the attached questionnaires. The quantitativedata that we are going to analyze from the touch screensincludes the number of times they are using the touch screen,what they are touching, and at what time during the session.The quantitative data we are going to obtain from the videosare:• The number of times and how long every kid is talkingduring all sessions.• The distance between kids during all sessions• Eye tracking and facial states during all sessionsB. Machine Learning as Descriptor Mining and Rule Ex-tractorThe main purpose of the Machine Learning algorithm isto classify all information to extract a set of rules that willdefine the model. In this project, we have data from twodifferent kinds: qualitative and quantitative.1) Modelling quantitative data: Similar to [8], we haveChildren Assitant Agents (CAA) placed in the cloud systemand connected to its individual Social Robot. All CAAsare linked to an Information Management Agent (IMA) thatreceive all information from the CAAs to build the model.Because the model is scalable to different cloud sites, wecan have multiples IMAs.Because we are searching for two different models, IMA’sfunctionality is based two strategies:• The social skills model rules are better predictable, sowe are based in [9] UCS, accuracy-based Michigan-style LCS that takes advantage of knowing the classof the training instances. UCS evolves a population ofclassifiers based on rules. Once the quality of the rulesis proved the model can be extracted from the collectionof rules and each classifier.• For the engineering skills model we have a greater levelof uncertainty, so we decided to use first use a systemto classify and then a system to extract rules [10], [11]2) Modelling qualitative data: We have used multicriteriadecision-making systems, which would be the second partof modeling, as to the assessment models or from differentexperts [12]III. RESULTS AND CONCLUSIONSCan be the model used only with the data obtainedfrom the social robot as data logger? Because we had onlyfour children in all sessions during the first workshop, thisis a hard hypothesis to answer. Results showed that thequantitative data we obtained was potentially good. Howeverbecause we used different robotic platforms (AISOY, NAO,and a custom robot), and because the number of children wassmall the results were inconsistent. In any case, we testedthe technology, and it shows us that we need to mix thequalitative data with the quantitative data in a more integratedway.We expect to get a consistent model as long as we areusing only one platform with more children.ACKNOWLEDGMENTOur thanks to LEGO Foundation and TTT Outreach, ThinkTank Team, Samsung Research America to fund the project.Also to CASPAN Center in Panama to cover part of theresearch project there.REFERENCES[1] Wainer, Joshua, et al. ”The effectiveness of using a robotics classto foster collaboration among groups of children with autism in anexploratory study.” Personal and Ubiquitous Computing 14.5 (2010):445-455.[2] Wainer, Joshua, et al. ”Collaborating with Kaspar: Using an au-tonomous humanoid robot to foster cooperative dyadic play amongchildren with autism.” Humanoid Robots (Humanoids), 2010 10thIEEE-RAS International Conference on. IEEE, 2010.[3] Diehl, Joshua J., et al. ”The clinical use of robots for individualswith autism spectrum disorders: A critical review.” Research in autismspectrum disorders 6.1 (2012): 249-262.[4] Beder, Sharon. ”Beyond technicalities: Expanding engineering think-ing.” Journal of Professional Issues in Engineering Education andPractice 125.1 (1999): 12-18.[5] Brophy, Sean, et al. ”Advancing engineering education in P12 class-rooms.” Journal of Engineering Education 97.3 (2008): 369-387.[6] Gardner, Howard. The unschooled mind: How children think and howschools should teach. Basic books, 2011.[7] Saarinen, Esa, and Raimo P. Hmlinen. ”Systems intelligence: Connect-ing engineering thinking with human sensitivity.” Systems intelligencein leadership and everyday life (2007): 51-78.[8] Navarro, Joan, et al. ”A Cloud robotics architecture to foster individualchild partnership in medical facilities.” (2013).[9] Orriols-Puig, Albert, and Ester Bernad-Mansilla. ”Revisiting ucs:Description, fitness sharing, and comparison with xcs.” LearningClassifier Systems. Springer Berlin Heidelberg, 2008. 96-116.[10] Diederich, Joachim, ed. Rule extraction from support vector machines.Vol. 80. Springer Science & Business Media, 2008.[11] Nez, Haydemar, Cecilio Angulo, and Andreu Catal. ”Rule extractionfrom support vector machines.” ESANN. 2002.[12] J. Nguyen, G. Sanchez-Hernandez, N. Agell and C. Angulo InsERT,the Inspirational Expert Recommender Tool. IEEE International Con-ference on Fuzzy Systems, FUZZ-IEEE 2015 [Accepted]

Modelling social skills and problem solving strategies used by children with ASD through cloud connected social robots as data loggers: first modelling approach

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