A Case Study of the Animal-Assisted Therapy for the Changes of Social Interaction of an Elementary School Student with Autism Spectrum Disorder

2019 ◽  
Vol 35 (2) ◽  
pp. 1-29
Author(s):  
Hye Bin Ko ◽  
Eun Kyung Kim
2020 ◽  
pp. 153465012098345
Author(s):  
Mirela Cengher ◽  
Joy C. Clayborne ◽  
Adrianna E. Crouch ◽  
Julia T. O’Connor

Over 60% of children diagnosed with selective mutism are also diagnosed with Autism Spectrum Disorder. Previous research established that behavioral interventions are effective at increasing speech in children with both diagnoses. However, few studies conducted assessments to determine environmental variables that inhibit speech, and such assessments are necessary for the development of effective and efficient treatments. This case study describes an assessment that evaluated the function(s) of selective mutism. The results confirmed that the participant did not talk to avoid social interaction and that mutism occurred primarily in the presence of multiple, unfamiliar people. Our first treatment focused on increasing tolerance for social interaction, demonstrated by an increase in speech production in the presence of unfamiliar people. Our second treatment focused on increasing qualitative aspects of the participant’s speech (i.e., both responses and initiations). Finally, we taught the participant’s parents to implement the treatment in naturalistic settings, and the participant demonstrated generalization of treatment effects across people and settings. Implications for clinical practice and future research are discussed.


2014 ◽  
Author(s):  
◽  
Xianhui Wang

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Over the past decade 3D collaborative virtual learning has gained increasing attention from researchers and practitioners in educational technology. Learners experience of presence in collaborative activities and social interactions among learners are identified as key constructs for the social dimensions of 3D collaborative virtual learning. 3D Collaborative Virtual Learning Environments (CVLEs) are beginning to be used to support learning in a variety of disciplines, including social skills learning for individuals with Autism Spectrum Disorder (ASD). This case study explores 11 youth with ASD's experience of embodied social presence and reciprocal social interaction while learning social competence in a 3D CVLE-iSocial. The findings describe youth with ASD's 1) levels of embodied presence, embodied copresence, and embodied social presence; and 2) verbal and nonverbal reciprocal social interactions across the variety of Naturalistic Practice activities in iSocial. In addition, the results of this case study inform future design by indicating associations of design features of iSocial 3D CVLE with youth with ASD's experience of embodied social presence and characteristics of reciprocal social interaction.


2020 ◽  
Vol 8 (9) ◽  
pp. 928-932
Author(s):  
Anahit Bindra ◽  

Autism Spectrum Disorder (ASD) is a life-long, pervasive neuro-development disorder that begins early in childhood and lasts throughout a persons life. It is characterised by deficits in three core areas - communication (both verbal and nonverbal), social interaction, and behaviour (which is restricted and repetitive). Case study refers to the in-depth study of a particular case. A case study employs multiple methods for collecting information such as interview, observation and psychological tests from a variety of respondents who in some way or the other might be associated with the case and can provide useful information. The information was collected by interviewing the case as well as the special educator (the teacher who assists the child at Vasant Valley school). In the case study, the details of the symptoms, causes, treatment, prevention, and management of the respondent were documented.


Author(s):  
Jacqueline Peng ◽  
Mengge Zhao ◽  
James Havrilla ◽  
Cong Liu ◽  
Chunhua Weng ◽  
...  

Abstract Background Natural language processing (NLP) tools can facilitate the extraction of biomedical concepts from unstructured free texts, such as research articles or clinical notes. The NLP software tools CLAMP, cTAKES, and MetaMap are among the most widely used tools to extract biomedical concept entities. However, their performance in extracting disease-specific terminology from literature has not been compared extensively, especially for complex neuropsychiatric disorders with a diverse set of phenotypic and clinical manifestations. Methods We comparatively evaluated these NLP tools using autism spectrum disorder (ASD) as a case study. We collected 827 ASD-related terms based on previous literature as the benchmark list for performance evaluation. Then, we applied CLAMP, cTAKES, and MetaMap on 544 full-text articles and 20,408 abstracts from PubMed to extract ASD-related terms. We evaluated the predictive performance using precision, recall, and F1 score. Results We found that CLAMP has the best performance in terms of F1 score followed by cTAKES and then MetaMap. Our results show that CLAMP has much higher precision than cTAKES and MetaMap, while cTAKES and MetaMap have higher recall than CLAMP. Conclusion The analysis protocols used in this study can be applied to other neuropsychiatric or neurodevelopmental disorders that lack well-defined terminology sets to describe their phenotypic presentations.


2012 ◽  
Vol 5 (1) ◽  
pp. 38-47 ◽  
Author(s):  
Jill Locke ◽  
Connie Kasari ◽  
Erin Rotheram-Fuller ◽  
Mark Kretzmann ◽  
Jeffrey Jacobs

2014 ◽  
Vol 20 (1) ◽  
pp. 23-26 ◽  
Author(s):  
Marc Woodbury-Smith

SummaryIn medical practice it is crucial that symptom descriptions are as precise and objective as possible, which psychiatry attempts to achieve through its psychopathological lexicon. The term ‘autism spectrum disorder’ has now entered psychiatric nosology, but the symptom definitions on which it is based are not robust, potentially making reliable and valid diagnoses a problem. This is further compounded by the spectral nature of the disorder and its lack of clear diagnostic boundaries. To overcome this, there is a need for a psychopathological lexicon of 'social cognition’ and a classification system that splits rather than lumps disorders with core difficulties in social interaction.


2021 ◽  
Vol 5 (10) ◽  
pp. 57
Author(s):  
Vinícius Silva ◽  
Filomena Soares ◽  
João Sena Esteves ◽  
Cristina P. Santos ◽  
Ana Paula Pereira

Facial expressions are of utmost importance in social interactions, allowing communicative prompts for a speaking turn and feedback. Nevertheless, not all have the ability to express themselves socially and emotionally in verbal and non-verbal communication. In particular, individuals with Autism Spectrum Disorder (ASD) are characterized by impairments in social communication, repetitive patterns of behaviour, and restricted activities or interests. In the literature, the use of robotic tools is reported to promote social interaction with children with ASD. The main goal of this work is to develop a system capable of automatic detecting emotions through facial expressions and interfacing them with a robotic platform (Zeno R50 Robokind® robotic platform, named ZECA) in order to allow social interaction with children with ASD. ZECA was used as a mediator in social communication activities. The experimental setup and methodology for a real-time facial expression (happiness, sadness, anger, surprise, fear, and neutral) recognition system was based on the Intel® RealSense™ 3D sensor and on facial features extraction and multiclass Support Vector Machine classifier. The results obtained allowed to infer that the proposed system is adequate in support sessions with children with ASD, giving a strong indication that it may be used in fostering emotion recognition and imitation skills.


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