Conference Paper

Choreografish: Co-designing a Choreography-based Therapeutic Virtual Reality System with Youth Who Have Autism Spectrum Advantages

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Abstract

Choreografish is a virtual reality, therapeutic arts engagement leveraging participatory research and design to collaborate with young adults with autism spectrum disorder (ASD). The research team was motivated by the social anxiety some with ASD have, and the attendant difficulties accessing art forms that may actually play well to Autism Spectrum Advantages (ASA). This project was co-designed with youth with ASA to explore the use of VR and choreographic thinking to empower users and designers to engage with the arts and self-manage anxiety. This paper describes the project, and gives a brief design history of Choreografish.

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... Out of the included 94 studies, 22 (23%) studies used forms of AI other than robotics . Of these, 15 included visualization and VR applications, such as an immersive virtual learning program [107,108,[112][113][114][115][116][117][118][119][120][121][122][123][124]; 8% (7/94) of interventions included neither robotics nor visualization and VR [109][110][111][125][126][127][128]. Examples of such interventions are a framework for speech-to-sign language translation for children with hearing impairments [127] and the design of a virtual space for hospitalized children to meet with their peers [108]. ...
... As for mode of delivery, most of the included studies described an intervention delivered in-person (84/94, 89%) [35][36][37][38][39][40][41][42][43][44][45][46][47][48][49][50][86][87][88][89][90][91]93,94,[96][97][98][99][101][102][103][104][105][106][107]109,[111][112][113][114][115][116][117][118][119][120][121][122][123][124]127], mainly using a one-on-one approach. A total of 11% (10/94) of included studies evaluated an AI intervention that was delivered remotely [51,85,92,95,100,108,110,125,126,128]. ...
... Most AI interventions were tailored to a category of individuals (ie, categorical personalization) such as by a diagnostic group (93/94, 99%) [111][112][113][114][115][116][117][118][119][120][121][122][123][124][125][126][127][128], using implicit (ie, automatically personalized: 45/94, 48%) [35, 36, 38, 43-46, 48-50, 52, 55-57, 60, 64-66, 70, 74-77, 79, 81-84, 87, 89-91, 97, 101, 104-107, 114, 117, 120, 125-128] or explicit (ie, manually personalized: 48/94, 51%) approaches [37, 39-42, 47, 51, 53, 54, 58, 59, 61-63, 67-69, 71-73, 78, 80, 85, 86, 88, 92-96, 98-100, 102, 103, 108, 109, 111-113, 115, 116, 118, 119, 121-124]. For example, Yee et al [35] designed a robotic platform for children with ASD by tailoring it to the needs typically described by this diagnostic group. ...
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Background In the last decade, there has been a rapid increase in research on the use of artificial intelligence (AI) to improve child and youth participation in daily life activities, which is a key rehabilitation outcome. However, existing reviews place variable focus on participation, are narrow in scope, and are restricted to select diagnoses, hindering interpretability regarding the existing scope of AI applications that target the participation of children and youth in a pediatric rehabilitation setting. Objective The aim of this scoping review is to examine how AI is integrated into pediatric rehabilitation interventions targeting the participation of children and youth with disabilities or other diagnosed health conditions in valued activities. Methods We conducted a comprehensive literature search using established Applied Health Sciences and Computer Science databases. Two independent researchers screened and selected the studies based on a systematic procedure. Inclusion criteria were as follows: participation was an explicit study aim or outcome or the targeted focus of the AI application; AI was applied as part of the provided and tested intervention; children or youth with a disability or other diagnosed health conditions were the focus of either the study or AI application or both; and the study was published in English. Data were mapped according to the types of AI, the mode of delivery, the type of personalization, and whether the intervention addressed individual goal-setting. Results The literature search identified 3029 documents, of which 94 met the inclusion criteria. Most of the included studies used multiple applications of AI with the highest prevalence of robotics (72/94, 77%) and human-machine interaction (51/94, 54%). Regarding mode of delivery, most of the included studies described an intervention delivered in-person (84/94, 89%), and only 11% (10/94) were delivered remotely. Most interventions were tailored to groups of individuals (93/94, 99%). Only 1% (1/94) of interventions was tailored to patients’ individually reported participation needs, and only one intervention (1/94, 1%) described individual goal-setting as part of their therapy process or intervention planning. Conclusions There is an increasing amount of research on interventions using AI to target the participation of children and youth with disabilities or other diagnosed health conditions, supporting the potential of using AI in pediatric rehabilitation. On the basis of our results, 3 major gaps for further research and development were identified: a lack of remotely delivered participation-focused interventions using AI; a lack of individual goal-setting integrated in interventions; and a lack of interventions tailored to individually reported participation needs of children, youth, or families.
... This ideation process has been applied to a wide variety of interdisciplinary research, ranging from educational games for critical thinking [23] to crowd sourcing applications in healthcare [11]. One study of particular interest involved the use of the Design Box to develop a therapeutic VR application for youth with autism [2]. The study discussed how the Design Box allowed them to narrow down their design ideas and really focus on the audiences, problems, feelings and technologies, creating a promising application for the users. ...
... The study discussed how the Design Box allowed them to narrow down their design ideas and really focus on the audiences, problems, feelings and technologies, creating a promising application for the users. They put it "constraints, after all, breed creativity" [2] (p.05). ...
... by creating a shared vocabulary that all parties can understand. This is reinforced by multiple other studies using the Design Box highlighting how effective the process was on interdisciplinary collaborations [2,11,23,43]. ...
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