Summary of Mmasd+: a Novel Dataset For Privacy-preserving Behavior Analysis Of Children with Autism Spectrum Disorder, by Pavan Uttej Ravva et al.
MMASD+: A Novel Dataset for Privacy-Preserving Behavior Analysis of Children with Autism Spectrum Disorder
by Pavan Uttej Ravva, Behdokht Kiafar, Pinar Kullu, Jicheng Li, Anjana Bhat, Roghayeh Leila Barmaki
First submitted to arxiv on: 27 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces MMASD+, an enhanced open-source dataset that integrates diverse data modalities for monitoring progress in individuals with autism spectrum disorder (ASD). The dataset addresses privacy concerns by utilizing algorithms like Yolov8 and Deep SORT to distinguish between therapists and children. A Multimodal Transformer framework is proposed, achieving high accuracy in predicting action types (95.03%) and ASD presence (96.42%). This framework showcases the benefits of combining multiple data modalities for improved performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a special dataset called MMASD+ to help people with autism learn new things over time. The problem is that other researchers can’t easily compare their results because they don’t share their private data. The new dataset has lots of different types of information, like how bodies move and what actions are happening. It also uses special computer programs to tell apart the person helping and the child with autism. The paper shows that using all this information together helps machines make more accurate predictions about what’s happening. |
Keywords
» Artificial intelligence » Transformer
