A Wearable Social Interaction Aid for Children with Autism
April 19, 2020 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Nick Haber, Catalin Voss, Jena Daniels, Peter Washington, Azar Fazel, Aaron Kline, Titas De, Terry Winograd, Carl Feinstein, Dennis P. Wall
arXiv ID
2004.14281
Category
cs.HC: Human-Computer Interaction
Cross-listed
cs.CV,
cs.LG
Citations
0
Venue
arXiv.org
Last Checked
5 months ago
Abstract
With most recent estimates giving an incidence rate of 1 in 68 children in the United States, the autism spectrum disorder (ASD) is a growing public health crisis. Many of these children struggle to make eye contact, recognize facial expressions, and engage in social interactions. Today the standard for treatment of the core autism-related deficits focuses on a form of behavior training known as Applied Behavioral Analysis. To address perceived deficits in expression recognition, ABA approaches routinely involve the use of prompts such as flash cards for repetitive emotion recognition training via memorization. These techniques must be administered by trained practitioners and often at clinical centers that are far outnumbered by and out of reach from the many children and families in need of attention. Waitlists for access are up to 18 months long, and this wait may lead to children regressing down a path of isolation that worsens their long-term prognosis. There is an urgent need to innovate new methods of care delivery that can appropriately empower caregivers of children at risk or with a diagnosis of autism, and that capitalize on mobile tools and wearable devices for use outside of clinical settings.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Human-Computer Interaction
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Improving fairness in machine learning systems: What do industry practitioners need?
R.I.P.
π»
Ghosted
Identifying Stable Patterns over Time for Emotion Recognition from EEG
R.I.P.
π»
Ghosted
Questioning the AI: Informing Design Practices for Explainable AI User Experiences
R.I.P.
π»
Ghosted
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
R.I.P.
π»
Ghosted
Educational data mining and learning analytics: An updated survey
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted