ArticleScientific data2024
Engagnition: A multi-dimensional dataset for engagement recognition of children with autism spectrum disorder.
Article in Scientific data, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed, 15 citations in OpenAlex.
- Adaptive Educational Technologies for Students with Autism Spectrum Disorder: A Meta-analysis of Emotional and Social Engagement Outcomes.Journal of autism and developmental disorders · 2026Article
- Design and Iterative Development of Serious Exergames for Children With Autism Spectrum Disorder: Formative Multiple-Case Pilot Study.JMIR serious games · 2026Article
- Engagnition: A multi-dimensional dataset for engagement recognition of children with autism spectrum disorder.Scientific data · 2024Article
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Authors and funding
4 authors at 1 institution in 1 country.
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Abstract
Engagement plays a key role in improving the cognitive and motor development of children with autism spectrum disorder (ASD). Sensing and recognizing their engagement is crucial before sustaining and improving the engagement. Engaging technologies involving interactive and multi-sensory stimuli have improved engagement and alleviated hyperactive and stereotyped behaviors. However, due to the scarcity of data on engagement recognition for children with ASD, limited access to and small pools of participants, and the prohibitive application requirements such as robots, high cost, and expertise, implementation in real world is challenging. However, serious games have the potential to overcome those drawbacks and are suitable for practical use in the field. This study proposes Engagnition, a dataset for engagement recognition of children with ASD (N = 57) using a serious game, "Defeat the Monster," based on enhancing recognition and classification skills. The dataset consists of physiological and behavioral responses, annotated by experts. For technical validation, we report the distributions of engagement and intervention, and the signal-to-noise ratio of physiological signals.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.