Summary of Physics Augmented Tuple Transformer For Autism Severity Level Detection, by Chinthaka Ranasingha et al.
Physics Augmented Tuple Transformer for Autism Severity Level Detection
by Chinthaka Ranasingha, Harshala Gammulle, Tharindu Fernando, Sridha Sridharan, Clinton Fookes
First submitted to arxiv on: 27 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 proposed novel framework uses physics-informed neural networks to recognize Autism Spectrum Disorder (ASD) severity. The approach encodes subject behavior extracted from skeleton-based motion trajectories in a higher-dimensional latent space, using two decoders: physics-based and non-physics-based. A classifier recognizes ASD severity by leveraging the same latent space embeddings. This dual generative objective forces the network to compare actual behavior with normal child behavior governed by physical laws, aiding ASD recognition. The method achieves state-of-the-art performance on multiple ASD diagnosis benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Early diagnosis of Autism Spectrum Disorder (ASD) is important for children’s health and well-being. Current manual testing methods are labor-intensive, complex, and prone to human error. Researchers propose a new approach that uses physics-informed neural networks to recognize ASD severity. The method analyzes how children move and compares it to normal child behavior governed by physical laws. This helps identify ASD and its severity. The results show this method is better than others at diagnosing ASD. |
Keywords
» Artificial intelligence » Latent space
