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Enregistrement W2563750632 · doi:10.1176/appi.pn.2016.12b12

Smart Glasses May Improve More Than Eyesight in Children With ASD

2016· article· en· W2563750632 sur OpenAlexaboutno aff
Nick Zagorski

Notice bibliographique

RevuePsychiatric News · 2016
Typearticle
Langueen
DomaineNeuroscience
ThématiqueAutism Spectrum Disorder Research
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésAvatarPsychologySocial skillsAutism spectrum disorderAutismOrder (exchange)Social mediaMultimediaMedical educationInternet privacyComputer scienceWorld Wide WebMedicineHuman–computer interactionPsychotherapistDevelopmental psychology

Résumé

récupéré en direct d'OpenAlex

Back to table of contents Previous article Next article Tech TimesClinical and Research NewsFull AccessSmart Glasses May Improve More Than Eyesight in Children With ASDNick ZagorskiNick ZagorskiSearch for more papers by this authorPublished Online:13 Dec 2016https://doi.org/10.1176/appi.pn.2016.12b12AbstractAn engineering student in Toronto wants to use Google Glass technology to encourage more functional independence in autistic children.iStock/Dean MitchellChildren with autism spectrum disorder (ASD) tend to be drawn to technology—an affinity that some believe could be tapped to improve their social and behavioral skills.To date, however, most tech-based therapies for ASD, including mobile applications, video games, and even interactive robots (Psychiatric News, October 2, 2015) offer only an indirect means for improving social behaviors.Ben Kinsella, a graduate engineering student at Toronto’s Holland Bloorview Kids Rehabilitation Hospital, thinks that Google Glass—an Internet-connected eyewear device in which text appears on the interior of the lens—might offer a new way to coach patients with ASD through more direct social interactions.After hearing from parents of children with ASD that they were looking for new ways to encourage their children to engage with others, Kinsella developed a tablet-based game that simulated common real-world scenarios. In the game, the player is encouraged to interact with a digital avatar to place an order at a fast-food restaurant. After being asked “What would you like to order?” the tablet offers a series of prompts such as “I would like a hamburger, please” or “Do you have any specials today?” The player repeats one of these prompts aloud to continue the conversation. With each successful encounter with the avatar, the game adds elements to mimic a real-world conversation more closely; for example, at higher levels of the game, the game no longer offers prompts and background noise increases.Like other machine-learning programs, this application remembers the more popular choices the player makes over time and gradually offers more personalized options during conversations. Kinsella later adapted his application so that instead of relying on a digital avatar, the program would record and analyze questions directly from a human. “Many studies have shown that children with autism prefer touchscreens, so sticking with a tablet as the platform seemed a natural choice,” Kinsella said. But, he soon realized that programming the language software into Google Glass might encourage the children to look at their partner during a conversation—an important component of normal communication.Kinsella and his colleagues at Bloorview’s Autism Research Center recently conducted a focus group of software-enabled glasses with a group of older children (average age of 12) with ASD. As part of this focus group, the researchers evaluated the accuracy of the recording software, response time for each answer, and the comfortableness of the glasses.The results of the pilot were positive in terms of program accuracy and user satisfaction, Kinsella said, but more work needs to be done to refine the “intelligence” of the software.“What works with the glasses is that the children focus on the visual overlay and not the other person, so it does make the interaction more comfortable for the child,” he said.Kinsella has presented his device at several technology and science conferences, and among the comments he has heard from medical professionals is a concern that this listen-and-prompt technology may reinforce the behavioral rigidity often displayed in ASD children.Shawn Sidhu, M.D., is an assistant professor of psychiatry at the University of New Mexico who chaired a session featuring Kinsella at the American Academy of Child and Adolescent Psychiatry annual meeting in October. Sidhu emphasized that it is important for parents to view these glasses as a training opportunity, not as an assistive device like a hearing aid.“Using the glasses sparingly to encourage independence … should keep children from becoming reliant on them in order to have a conversation.” As an added measure, Kinsella is developing more intelligent prompting mechanisms, such as leaving key words blank so the wearer must think of what they want (“I would like a ____, please”) or occasionally incorporating a brief delay before prompts appear to encourage the wearer to speak freely. “What’s most important is that we get this right in terms of having a product that is supported by clinical evidence that can help kids and families with ASD,” he said. “There is a lot of tech for autism out there already, but we need to ensure that available technology is evidence based. This is a novel idea, but we don’t want to push it out for the sake of pushing it out. We want to make a difference.” ■ ISSUES NewArchived

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,027
Score d'incertitude au seuil0,647

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,014
Tête enseignante GPT0,280
Écart entre enseignants0,266 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2016
Routes d'admission1
Résumé présentoui

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