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A Learning Tracker using Digital Biomarkers for Autistic Preschoolers
FHNW University of Applied Sciences and Arts Northwestern Switzerland, Muttenz, Switzerland.
Kilburg Dialogue, Allschwil, Switzerland.
FHNW University of Applied Sciences and Arts Northwestern Switzerland, Olten, Switzerland.
FHNW University of Applied Sciences and Arts Northwestern Switzerland, Olten, Switzerland.
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2022 (English)In: Proceedings of the Society 5.0 Conference 2022 - Integrating Digital World and Real World to Resolve Challenges in Business and Society / [ed] Knut Hinkelmann; Aurona Gerber, EasyChair , 2022, p. 219-230Conference paper, Published paper (Refereed)
Abstract [en]

Preschool children, when diagnosed with Autism Spectrum Disorder (ASD), often ex- perience a long and painful journey on their way to self-advocacy. Access to standard of care is poor, with long waiting times and the feeling of stigmatization in many social set- tings. Early interventions in ASD have been found to deliver promising results, but have a high cost for all stakeholders. Some recent studies have suggested that digital biomarkers (e.g., eye gaze), tracked using affordable wearable devices such as smartphones or tablets, could play a role in identifying children with special needs. In this paper, we discuss the possibility of supporting neurodiverse children with technologies based on digital biomark- ers which can help to a) monitor the performance of children diagnosed with ASD and b) predict those who would benefit most from early interventions. We describe an ongoing feasibility study that uses the “DREAM dataset”, stemming from a clinical study with 61 pre-school children diagnosed with ASD, to identify digital biomarkers informative for the child’s progression on tasks such as imitation of gestures. We describe our vision of a tool that will use these prediction models and that ASD pre-schoolers could use to train certain social skills at home. Our discussion includes the settings in which this usage could be embedded. 

Place, publisher, year, edition, pages
EasyChair , 2022. p. 219-230
Series
EPiC Series in Computing, ISSN 2398-7340 ; 84
Keywords [en]
Autism Spectrum Disorder, Digital Biomarkers, machine learning, personalized medicine
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering Nursing
Research subject
Interaction Lab (ILAB)
Identifiers
URN: urn:nbn:se:his:diva-21351DOI: 10.29007/m2jxScopus ID: 2-s2.0-85133755454OAI: oai:DiVA.org:his-21351DiVA, id: diva2:1673648
Conference
Society 5.0, Integrating Digital World and Real World to Resolve Challenges in Business and Society, 2nd Conference, hybrid (online and physical) at the FHNW University of Applied Sciences and Arts Northwestern Switzerland from 20th to 22nd June 2022, Windisch, Switzerland
Note

"Our feasibility study is funded by the Swiss Innovation Agency Innosuisse under the grantnumber 60506.1 INNO-LS."

Available from: 2022-06-21 Created: 2022-06-21 Last updated: 2024-08-29Bibliographically approved

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Billing, Erik

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