Abstract
Background: Autism spectrum disorder (ASD) is a category of neurodevelopmental disorder characterized by persistent deficits in social communication and social interaction across multiple contexts as well as restricted, repetitive patterns of behaviour, interests, or activities. Social robots offer clinicians new ways to interact and work with people with ASD. Robot-Assisted Training (RAT) is a growing body of research in HRI, which studies how robots can assist and enhance human skills during a task-centred interaction. RAT systems have a wide range of application for children with ASD.
Aims: In a pilot RCT with an experimental group and a control group, research aims will be: to assess group differences in repetitive and maladaptive behaviours (RMBs), affective states and performance tasks across sessions and within each group; to assess the perception of family relationships between two groups before and post robot interaction; to develop a robotic app capable to run Raven’s Progressive Matrices (RPM), a test typically used to measure general human intelligence and to compare the accuracy of the robot to capture the data with that run by psychologists.
Material and Methods: Patients with mild or moderate level of ASD will be enrolled in the study which will last 3 years. The sample size is: 60 patients (30 patients will be located in the experimental group and 30 patients will be located in the control group) indicated by an evaluation of the estimated enrolment time. Inclusion criteria will be the following: eligibility of children confirmed using the Autism Diagnostic Observation Schedule −2; age ≥ 7 years; clinician judgment during a clinical psychology evaluation; written parental consent approved by the local ethical committee. The study will be conducted over 10 weeks for each participant, with the pretest and post test conducted during the first and last weeks of the study. The training will be provided over the intermediate eight weeks, with one session provided each week, for a total of 8 sessions. Baseline and follow-up evaluation include: socioeconomic status of families will be assessed using the Hollingshead scale; Social Communication Questionnaire (SCQ) will be used to screen the communication skills and social functioning in children with ASD; Vineland Adaptive Behavior Scale, 2nd edition (VABS) will be used to assess the capabilities of children in dealing with everyday life; severity and variety of children’s ripetitive behaviours will be also assessed using Repetitive Behavior Scale-Revised (RBS-R). Moreover, the perception of family relationships assessment will be run by Portfolio for the validation of parental acceptance and refusal (PARENTS).
Expected Results: 1) improbe communication skills; 2) reduced repetitive and maladaptive behaviors; 3) more positive perception of family relationships; 4) improved performance.
Conclusions: Robot-Assisted Training aims to train and enhance user (physical or cognitive) skills, through the interaction, and not assist users to complete a task thus a target is to enhance user performance by providing personalized and targeted assistance towards maximizing training and learning effects. Robotics systems can be used to manage therapy sessions, gather and analyse data and like interactions with the patient and generate useful information in the form of reports and graphs, thus are a powerful tool for the therapist to check patient’s progress and facilitate diagnosis.
You have full access to this open access chapter, Download conference paper PDF
Similar content being viewed by others
Keywords
1 Introduction
Autism spectrum disorder (ASD) is a category of neurodevelopmental disorder characterized by persistent deficits in social communication and social interaction across multiple contexts as well as restricted, repetitive patterns of behaviour, interests, or activities [1]. The care and social needs of preschool children with ASD (typically up to six years of age), in particular, are significant [2, 3], usually extend to parents and siblings [2, 4, 5], and require substantial community resources [2, 6, 7]. Estimated costs per year for children affected by Autistic Spectrum Disorders (ASD) in the US are expected to be between $ 11.5 billion – $ 60.9 billion (2011 US $), representing a significant economic burden from medical care to private education [8, 9]. An increasing amount of research has investigated the use of digital interventions for support and treatment of ASD individuals under terms such as digital health [10], computer-based [11, 12], computer-assisted [13], innovative technology-based [14] and technology-aided interventions [15]. Devices like computers, smartphones, wearable technologies, virtual reality, robotics and tablets [13] have been used. The interventions attempt to teach or train e.g. communication [9], social and emotional skills [10] and academic skills [11]. The use of digital interventions may be beneficial for ASD individuals, since such interventions are consistent, predictable, and without social interaction, which is preferred by ASD individuals [16]. Computer-assisted and robot-assisted therapy is infiltrating the social skills teaching environment, being trailed or incorporated into therapy by a variety of professions to help teach the child with ASD [17,18,19]. Validation of the effectiveness of computer-aided therapies to teach social skills is warranted to justify the quality of these interventions. Useful technologies will likely proliferate further into therapy regimens, offering new models and assistance to those who serve these children and their families. Animal-like robots and humanoid robot have received especially notable acceptance in therapeutic settings. The notion that children with ASD prefer robots as tutors to improve their social interaction and communication abilities is supported by recent studies. Indeed, the research focused on developing a very promising form of intervention called robot-assisted therapy. This therapy has some challenges, e.g., the necessary flexibility and adaptability to real unrestricted therapeutic settings. The most frequent deficiency to children with autism and mental disability is social attention, which includes the difficulty of focusing good visual attention. Di Nuovo and his colleagues [20] examined the use of a new deep learning neural network architectures to automatically determine whether a child-focused on visual attention during a therapeutic session, indicating their commitment. They used the NAO humanoid robot [21] for their research and have proposed the use of computational intelligence techniques to increase robot capabilities for greater adaptability and flexibility, enabling the robot to be integrated into any therapeutic environment, according to the specific needs of the therapist and the individual child. A study by Huskens et coll. Utilized a robot-mediated intervention based on LEGO® Therapy to study the impact on collaborative play behaviour [22, 23], using NAO robot. The robot reinforced collaboration and offered prompts.
In another study by Srinivasan et al. [24] utilizing the rhythm and robotic therapy intervention with NAO and Rovio, the outcome targets were repetitive behaviours and affective states in children with ASD. After training, the rhythm group reduced negative behaviours. Affective state results indicated the rhythm and robotic groups demonstrated greater interested affection across all sessions. Negative affection was decreased and interested affection increased in the rhythm group after training. Other studies have reported improvement in communication, social skills and gestural delay for a child with ASD after robot therapy intervention [25,26,27,28,29,30,31]. These studies highlight the uses of robot-assisted interventions to teach social skills to children with ASD. The systematic review by Grossard and colleagues [32] reported excellent state of the art in the topic ICT and autism care from 2017 to 2018. They analyzed serious games and social robots. The authors noted children with ASD have a specific need for predictability, visual support, and a sequential presentation of information, which aligns well with the use of social robots. They concluded that social robots offer clinicians new ways to interact and work with people with ASD. Robot-Assisted Training (RAT) is a growing body of research in HRI, which studies how robots can assist and enhance human skills during a task-centred interaction. RAT systems have a wide range of application from physical assistance in post-stroke rehabilitation and robotic prosthetics [33], to cognitive training for patients suffering from dementia, Alzheimer's disease [34, 35], MCI and intervention and therapy for children with ASD. Socially Assistive Robotics (SAR) is also employed for language learning and children education [36, 37]. HRI is a multidisciplinary research field that involves human-machine interaction, machine learning, data mining, computer vision as well as psychology and educational sciences, kinesiology, occupational therapy and others. The main difference from other assistive robotic systems is that Robot-Assisted Training aims to train and enhance user (physical or cognitive) skills, through the interaction, and not assist users to complete a task (Activities of Daily Living) thus a target is to enhance user performance by providing personalized and targeted assistance towards maximizing training and learning effects. Applied behaviour analysis (ABA) is one of the most extended therapies for the treatment of autism, consisting of improving specific behaviours which are divided into simple and repetitive tasks that are presented sequentially and strategically while measuring and analysing the patient’s performance during the therapy [38]. Robotics systems can be used to manage therapy sessions, gather and analyse data and like interactions with the patient and generate useful information in the form of reports and graphs, thus are a powerful tool for the therapist to check patient’s progress and facilitate diagnosis. The visual appeal of the robotics platform is a key factor to engaging the attention of children with autism; indeed those robots tend to use bright colours, rotating mechanical parts, striking shapes and lights [39].
2 Objective
In a pilot RCT with an experimental group and a control group, research aims will be:
-
to assess group differences in repetitive and maladaptive behaviours (RMBs), affective states and performance tasks across sessions and within each group;
-
to assess the perception of family relationships between two groups before and post robot interaction;
-
to develop a robotic app capable to run Raven’s Progressive Matrices (RPM), a test typically used to measure general human intelligence and to compare the accuracy of the robot to capture the data with that run by psychologists.
3 Application Field
Patients with mild or moderate level of ASD to enrol in Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo (FG).
Duration of the study: 3 years.
Sample size: 60 patients (30 patients will be located in the experimental group and 30 patients will be located in the control group) indicated by an evaluation of the estimated enrolment time.
Inclusion criteria will be the following:
-
1)
eligibility of children confirmed using the Autism Diagnostic Observation Schedule −2, a gold standard diagnostic assessment for ASD [40];
-
2)
age ≥ 7 years;
-
3)
clinician judgment during a clinical psychology evaluation;
-
4)
written parental consent approved by the local ethical committee.
4 Description of the Process Phases, Indicators and Monitoring
4.1 Procedure
The study will be conducted over 10 weeks for each participant, with the pretest and posttest conducted during the first and last weeks of the study. The training will be provided over the intermediate eight weeks, with one session provided each week, for a total of 8 sessions.
4.2 Baseline and Follow-up Evaluation
The socioeconomic status of families will be assessed using the Hollingshead scale [41]. The Social Communication Questionnaire (SCQ) [42] was used to screen the communication skills and social functioning in children with ASD. Vineland Adaptive Behavior Scale, 2nd edition (VABS) [43] will be used to assess the capabilities of children in dealing with everyday life (i.e., communication skills, motor skills, functionalities needed in everyday life, and socialization). It will be also assessed the severity and variety of children’s repetitive behaviours using the parent questionnaire, Repetitive Behavior Scale-Revised (RBS-R) [44]. Moreover, the perception of family relationships assessment will be run by Portfolio for the validation of parental acceptance and refusal (PARENTS) [45].
4.3 Robot-Mediated Training
Among android social assistive robots, Pepper from Softbank Robotics is a humanoid (height = 1.21 m and width = 0.48 m) that has almost the same articulations as a human, except for its mobile base and the impossibility of moving every finger independently. It has four microphones, two loudspeakers, two RGB cameras and a depth sensor (Asus Xtion). It has tactile sensors in the head and the back of its hands. It has a speech recognition engine that is able of identifying multiple variations in the human voice and a speech to text module that makes it talk with people, as well as face detection and recognition and emotion detection engines. It is also provided with a tablet, enabling it in showing interactive content. Nao, Pepper’s little brother with similar software characteristics, is also a humanoid (height = 0.57 m and weight = 5 kg) appearing as of a human toddler and already widely adopted in ABA with ASD. Pepper and Nao are equipped with cameras and software for eye-tracking, face detection and recognition however it will not be employed in this study (Fig. 1).
Robot-assisted therapy today is available through applications running on the robot Pepper itself; the Research Hospital IRCCS “Casa Sollievo della Sofferenza” is licensed with a solution named Robomate, developed by BehaviorLabs. Robomate is an LMS (Learning Management System) platform for humanoid robots, created for the following purposes (Fig. 2):
-
Simplify the use of robots by clinicians, therapists and educators
-
Realize an easy and intuitive platform for the human-machine interaction
-
Handle e-learning contents and “edutainment”
-
Manage contents
-
Track and store results of the executed sessions and patient data
-
Generate reports and statistics on the results of the executed sessions
Robomate is then tailored for the use of a humanoid robot, representing a direct communication channel between the content and the child, and a tablet device as a reinforcing and feedback element where the child himself can answer and interact in the session. In this sense, the robot and the tablet work together both as executors and catchers of feedbacks during the training sessions. Through the iPad app Robomate, a therapist can register patient data and sessions as well as trigger predefined animations on the robot to catch the attention during a test session. Telepresence on the app through Pepper’s mics and cameras is also available (Fig. 3).
Following, a description of the exercises available on the robot through Robomate.
4.4 Interactive Sessions with the Robot
Interactive sessions of the child with the robot will take place after the baseline session and before the follow-up evaluation session. Three standard session types have been designed with a predefined set of tests selected from the table above that the therapist will administer using the robot (Table 1 and 2):
In the eight interactive sessions with Pepper, the schemas proposed in the table above will be administered as follows in Fig. 4.
5 Ethical Considerations
5.1 General Ethical Aspects in the Conduction of the Study
Declaration of Helsinki.
The current version of the Declaration of Helsinki (2013) is a reference for the ethical aspects of this clinical study and it will be respected by those employed in this research study.
5.2 Ethical Committee
The Principal Investigator of the study will present to the Ethical Committee of the Research Hospital IRCCS “Casa Sollievo della Sofferenza” in San Giovanni Rotondo. The study protocol including documentation relating to the information to be provided to patients and forms for obtaining informed consent to medical treatment, informed consent to the processing of personal data and any other document necessary for them to fulfil their responsibilities. The study will be started only after obtaining the written approval, dated and signed by the Ethical Committee, under the guidelines of Good Clinical Practice (Buona pratica Clinica, Italian Ministerial Decree No. 162 of July 15, 1997) and the Italian Ministerial Decree of March 18, 1998 “Linee Guida per l’istituzione ed il funzionanamento dei Comitati Etici” published in the Official Gazette No. 122 of May 28, 1998.
5.3 The Informed Consent Form and Information Sheet
Before carrying out any study procedure on a patient belonging to the centre, the Investigator must obtain the acquisition of all written consent. It is the Investigator's responsibility to provide comprehensive information relating to the study's operating procedures. The documents that identify the subject will be kept confidential and, under EU Regulation 2016/679 (GDPR) and Legislative Decree 196/2003, will not be made publicly available or communicated to unauthorized subjects. If the results of the study are published, the identity of the subject will remain secret. The patient must be informed that participation in the study is voluntary and that refusal to participate does not imply any penalty, that he has the right to terminate his participation in the study at any time and that this decision will not affect future care. Before the start of the study, the Investigator will deliver to each patient’s parent the Information Sheet, containing the description of the study and the telephone number of the Investigator. The subject will be given sufficient time to be able to decide on possible participation in the study and to obtain any necessary clarification from the Investigator regarding the study. Furthermore, the patient’s parent must carefully read, and understand, what is present in the “Prospect - Informed Consent” form, section “Information on Data Processing”; subsequently, he/she may affix his written consent. If the patient’s parent is in favour of participating in the study, he/she will personally sign and date the “Prospect - Informed Consent” form. The Investigator will in turn sign the Informed Consent form. A copy of the aforementioned forms, duly signed, will be delivered to the patient’s parent.
5.4 Data Recording and Storage
The data collected through electronic devices and questionnaires will report the codes assigned to patients. The codes will be designed in such a way as not to allow inferring the identity of the subject to which they refer. All documents that could disclose the identity of patients will be kept by the Investigator in strict confidentiality, under EU Regulation 2016/679 (GDPR) and Legislative Decree 196/2003. All documents containing the original data and transcribed in the CRF will remain in the archives of the Unit of Child Neuropsychiatry of the Research Hospital IRCCS “Casa Sollievo della Sofferenza”. The signed originals of the forms, the hospital documentation and other documents relating to the study must be archived by the Investigator and kept in the limited access archives of the Unit of Child Neuropsychiatry “Casa Sollievo della Sofferenza”. No documents relating to the study may be destroyed without prior authorization from the investigators.
5.5 Financial Aspects
This kind of research will not entail any cost for the Italian National Health Service. As this is a spontaneous study, the participating investigators will not receive any compensation by way of reimbursement for the research activities carried out.
6 Conclusions
Robot-Assisted Training (RAT) aims to train and improve both physical and cognitive skills of the user, through interaction, so one of the goals of this study is to improve user performance by providing personalized and targeted assistance towards maximizing training and learning effects. Robotic systems can be used to manage therapy sessions, collect and analyze both data and parental interactions and perceptions of family relationships. One of the most disabling deficits of children with ASD is difficulty in communication and social interaction. Specifically, children with ASD may exhibit difficulties with social-emotional reciprocity; deficits in nonverbal communicative behaviors used for social interaction; and deficits in the development, management, and understanding of relationships. RAT appears to be a valuable support for improving communication skills. In addition, the study aims to reduce repetitive and maladaptive behaviors through the use of specific therapy sessions that will help children with ASD to manage repetitive and maladaptive patterns of behavior, interest or activities. The RAT supports the processing of useful information in the form of reports and graphs, proving to be a powerful tool for the therapist in order to monitor the patient's therapeutic progress and facilitate in a timely manner the formulation of a correct diagnosis, useful also for the multidisciplinary team (educational, psychological, medical).
References
American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders, 5th edn. American Psychiatric Association: Washington, DC, USA, (2013)
Hyman, S.L.; Levy, S.E.; Myers, S.M.: Identification, evaluation, and management of children with autism spectrum disorder. Pediatrics 145 (2020)
World Health Organization. Meeting Report: Autism Spectrum Disorders & Other Developmental Disorders: From Raising Awareness to Building Capacity; World Health Organization: Geneva, Switzerland (2013)
Rojas-Torres, L.P., Alonso-Esteban, Y., Alcantud-Marín, F.: Early intervention with parents of children with autism spectrum disorders: a review of programs. Children 7, 294 (2020)
Seymour, M., Wood, C., Giallo, R., Jellett, R.: Fatigue, stress and coping in mothers of children with an autism spectrum disorder. J. Autism Dev. Disord. 43, 1547–1554 (2013). https://doi.org/10.1007/s10803-012-1701-y
Cakir, J., Frye, R.E., Walker, S.J.: The lifetime social cost of autism: 1990–2029. Res. Autism Spectr. Disord. 72, 101502 (2020)
Tachibana, Y., et al.: A systematic review and meta-analysis of comprehensive interventions for pre-school children with autism spectrum disorder (ASD). PLoS ONE 12, e0186502 (2017)
Yousif, J., Kazem, H.A., Chaichan, M.: Evaluation implementation of humanoid robot for autistic children: a review. Int. J. Comput. Appl. Sci. 6, 412–420 (2019)
Vellonen, V., Kärnä, E., Virnes, M.: Communication of children with autism in a technology-enhanced learning environment. Procedia Soc. Behav. Sci. 69, 1208–1217 (2012)
Hollis, C., et al.: Annual research review: digital health interventions for children and young people with mental health problems: a systematic and meta-review. J. Child Psychol. Psychiatry 58(4), 474–503 (2017)
Ramdoss, S., et al.: Use of computer-based interventions to teach communication skills to children with autism spectrum disorders: a systematic review. J. Behav. Educ. 20(1), 55–76 (2011). https://doi.org/10.1007/s10864-010-9112-7
Ramdoss, S., Machalicek, W., Rispoli, M., Mulloy, A., Lang, R., O’Reilly, M.: Computer-based interventions to improve social and emotional skills in individuals with autism spectrum disorders: a systematic review. Dev. Neurorehabil. 15(2), 119–135 (2012)
Root, J.R., Stevenson, B.S., Davis, L.L., Geddes-Hall, J., Test, D.W.: Establishing computer-assisted instruction to teach academics to students with autism as an evidence-based practice. J. Autism Dev. Disord. 47(2), 275–284 (2017). https://doi.org/10.1007/s10803-016-2947-6
Grynszpan, O., Weiss, P.L., Perez-Diaz, F., Gal, E.: Innovative technology-based interventions for autism spectrum disorders: a meta-analysis. Autism 18(4), 346–361 (2014)
Odom, S.L., Thompson, J.L., Hedges, S., Boyd, B.A., Dykstra, J.R., Duda, M.A., et al.: Technology-aided interventions and instruction for adolescents with autism spectrum disorder. J. Autism Dev. Disord. 45(12), 3805–3819 (2015). https://doi.org/10.1007/s10803-014-2320-6
Golan, O., Baron-Cohen, S.: Systemizing empathy: teaching adults with Asperger syndrome or high-functioning autism to recognize complex emotions using interactive multimedia. Dev. Psychopathol. 18(2), 591–617 (2006)
Virginia Department of Education. Guidelines for Educating Students with Autism Spectrum Disorders. Commonwealth of Virginia, Department of Education; Richmond, VA, USA (2010)
Boucenna, S., et al.: Interactive technologies for autistic children: a review. Cogn. Comput. 6, 722–740 (2014). https://doi.org/10.1007/s12559-014-9276-x
Wong, C., et al.: Evidence-Based Practices for Children, Youth, and Young Adults with Autism Spectrum Disorder. The University of North Carolina, Frank Porter Graham Child Development Institute, Autism Evidence-Based Practice Review Group, Chapel Hill, NC, USA (2013)
Di Nuovo, A., Conti, D., Trubia, G., Buono, S., Di Nuovo, S.: Deep learning systems for estimating visual attention in robot-assisted therapy of children with autism and intellectual disability. Robotics 7, 25 (2018)
Aldebaran Robotics Nao. https://www.softbankrobotics.com/emea/en/nao. Accessed 06 April 2021
Huskens, B., Palmen, A., Van der Werff, M., Lourens, T., Barakova, E.: Improving collaborative play between children with autism spectrum disorders and their siblings: the effectiveness of a robot-mediated intervention based on Lego® therapy. J. Autism Dev. Disord. 45, 3746–3755 (2015). https://doi.org/10.1007/s10803-014-2326-0
LeGoff, D.B.: Use of LEGO® as a therapeutic medium for improving social competence. J. Autism Dev. Disord. 34, 557–571 (2004). https://doi.org/10.1007/s10803-004-2550-0
Srinivasan, S.M., Park, I.K., Neelly, L.B., Bhat, A.N.: A comparison of the effects of rhythm and robotic interventions on repetitive behaviors and affective states of children with autism spectrum disorder (ASD). Res. Autism Spectr. Disord. 18, 51–63 (2015)
Taheri, A., Meghdari, A., Alemi, M., Pouretemad, H.R.: Clinical interventions of social humanoid robots in the treatment of a pair of high-and low-functioning autistic Iranian twins. Sci. Iran. 25, 1197–1214 (2018)
Taheri, A., Meghdari, A., Alemi, M., Pouretemad, H.R.: Human-robot interaction in autism treatment: a case study on three pairs of autistic children as twins, siblings, and classmates. Int. J. Soc. Robot. 10, 93–113 (2018). https://doi.org/10.1007/s12369-017-0433-8
Van Straten, C.L., Smeekens, I., Barakova, E., Glennon, J., Buitelaar, J., Chen, A.J.: Effects of robots’ intonation and bodily appearance on robot-mediated communicative treatment outcomes children with autism spectrum disorder. Pers. Ubiquit. Comput. 22, 379–390 (2018). https://doi.org/10.1007/s00779-017-1060-y
David, D.O., Costescu, C.A., Matu, S., Szentagotai, A., Dobrean, A.: Developing joint attention for children with autism in robot-enhanced therapy. Int. J. Soc. Robot. 10, 595–605 (2018). https://doi.org/10.1007/s12369-017-0457-0
Desideri, L., et al.: Using a humanoid robot as a complement to interventions for children with autism spectrum disorder: a pilot study. Adv. Neurodev. Disord. 2, 273–285 (2018). https://doi.org/10.1007/s41252-018-0066-4
Saadatzi, M.N., Pennington, R.C., Welch, K.C., Graham, J.H.: Small-group technology-assisted instruction: virtual teacher and robot peer for individuals with autism spectrum disorder. J. Autism Dev. Disord. 48, 3816–3830 (2018). https://doi.org/10.1007/s10803-018-3654-2
So, W.C., et al.: Robot-based intervention may reduce delay in the production of intransitive gestures in Chinese-speaking preschoolers with autism spectrum disorder. Mol. Autism. 9–34 (2018)
Grossard, C., Palestra, G., Xavier, J., Chetouani, M., Grynszpan, O., Cohen, D.: ICT and autism care: state of the art. Curr. Opin. Psychiatry 31(6), 474–483 (2018)
Tsiakas, K., Kyrarini, M., Karkaletsis, V., Makedon, F., Korn, O.: A taxonomy in robot-assisted training: current trends, needs challenges. Technologies 6, 119 (2018). https://doi.org/10.3390/technologies6040119
Wada, K., Shibata, T., Musha, T., Kimura, S.: Robot therapy for elders affected by dementia. IEEE Eng. Med. Biol. Mag. 27, 53–60 (2008)
Jøranson, N., Pedersen, I., Rokstad, A.M.M., Ihlebæk, C.: Effects on symptoms of agitation and depression in persons with dementia participating in robot-assisted activity: a cluster-randomized controlled trial. J. Am. Med. Direct. Assoc. 16, 867–873 (2015)
Lee, S., et al.: On the effectiveness of robot-assisted language learning. ReCALL 23, 25–58 (2011)
Han, J.: Emerging technologies: robot assisted language learning. Lang. Learn. Technol. 16, 1–9 (2012)
Kasari, C., Lawton, K.: New directions in behavioral treatment of autism spectrum disorders. Curr. Opin. Neurol. 23, 137 (2010)
Cabibihan, J.J., Javed, H., Ang, M., Aljunied, S.M.: Why robots? A survey on the roles and benefits of social robots in the therapy of children with autism. Int. J. Soc. Robot. 5, 593–618 (2013). https://doi.org/10.1007/s12369-013-0202-2
Lord, C., Rutter, M., DiLavore, P.C., Risi, S., Gotham, K., Bishop, S.L.: Autism Diagnostic Observation Schedule, 2nd edn. (ADOS-2) manual (part 1): Modules 1–4. Western Psychological Services Torrance, CA (2012)
Hollingshead, A.: Four Factor Index of Social Status. Yale University, New Haven, CT (1975)
Rutter, M., Bailey, A., Lord, C.: The social communication questionnaire: manual western psychological services (2003)
Sparrow, S.S., Cicchetti, D.V., Balla, D.A.: The Vineland Adaptive Behavior Scales, 2nd edn. Springer, Cham (2005)
Lam, K.S., Aman, M.G.: The repetitive behavior scale-revised: Independent validation in individuals with autism spectrum disorders. J. Autism Dev. Disord. 37(5), 855–866 (2007). https://doi.org/10.1007/s10803-006-0213-z
Khaleque, A., Rohner, R.P.: Transnational relations between perceived parental acceptance and personality dispositions of children and adults: a meta-analytic review. Pers. Soc. Psychol. Rev. 16(2), 103–115 (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
Copyright information
© 2022 The Author(s)
About this paper
Cite this paper
D’Onofrio, G., Petito, A., Calvio, A., Toto, G.A., Limone, P. (2022). Robot Assistive Therapy Strategies for Children with Autism. In: Limone, P., Di Fuccio, R., Toto, G.A. (eds) Psychology, Learning, Technology. PLT 2022. Communications in Computer and Information Science, vol 1606. Springer, Cham. https://doi.org/10.1007/978-3-031-15845-2_7
Download citation
DOI: https://doi.org/10.1007/978-3-031-15845-2_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-15844-5
Online ISBN: 978-3-031-15845-2
eBook Packages: Computer ScienceComputer Science (R0)