Second Version of Google Glass as a Wearable Socio-Affective Aid: Positive School Desirability, High Usability, and Theoretical Framework in a Sample of Children with Autism
Bibliographic record
Abstract
BACKGROUND: Computerized smartglasses are being developed as an assistive technology for daily activities in children and adults with autism spectrum disorder (ASD). While smartglasses may be able to help with educational and behavioral needs, their usability and acceptability in children with ASD is largely unknown. There have been reports of negative social perceptions surrounding smartglasses use in mainstream populations, a concern given that assistive technologies may already carry their own stigma. Children with ASD may also have a range of additional behavioral, developmental, and social challenges when asked to use this emerging technology in school and home settings. OBJECTIVE: The usability and acceptability of Glass Enterprise Edition (Glass), the successor to Google Glass smartglasses, were explored in children with ASD and their caregivers. METHODS: Eight children with ASD and their caregivers were recruited to attend a demonstration session with Glass smartglasses the week they were publicly released. The children had a wide range of ability, including limited speech to speaking, and represented a full range of school ages (6 to 17 years). Children and caregivers were interviewed about their experience of using the smartglasses and whether they would use them at school and home. RESULTS: All 8 children succeeded in using Glass and did not feel stressed (8/8, 100%) or experience any overwhelming sensory or emotional issues during the session (8/8, 100%). All 8 children (8/8, 100%) endorsed that they would be willing to wear and use the device in both home and school settings. Caregivers felt the experience was fun for the children (8/8, 100%), and most caregivers felt the experience was better than they had expected (6/8, 75%). CONCLUSIONS: A wide age and ability range of children with ASD used Glass immediately after it was released and found it to be usable and acceptable. Despite concerns about potential stigma or social acceptability, all of the children were prepared to use the technology in both home and school settings. Encouragingly, most caregivers noted a very positive response. There were no behavioral, developmental, or social- or stigma-related concerns during or after the session. Smartglasses may be a useful future technology for children with ASD and are readily accepted for use by children with ASD and their caregivers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".