Summary of Multimodal Ensemble with Conditional Feature Fusion For Dysgraphia Diagnosis in Children From Handwriting Samples, by Jayakanth Kunhoth et al.
Multimodal Ensemble with Conditional Feature Fusion for Dysgraphia Diagnosis in Children from Handwriting Samples
by Jayakanth Kunhoth, Somaya Al-Maadeed, Moutaz Saleh, Younes Akbari
First submitted to arxiv on: 25 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 Machine learning educators may be interested in a novel approach that combines offline and online handwriting data to diagnose developmental dysgraphia. The study proposes a multimodal machine learning method that trains separate SVM and XGBoost classifiers on online and offline features, as well as implements feature fusion and soft-voted ensemble. The conditional feature fusion method intelligently combines predictions from online and offline classifiers, selectively incorporating feature fusion when confidence scores fall below a threshold. This approach achieves an accuracy of 88.8%, outperforming single-modal SVMs by 12-14%, existing methods by 8-9%, and traditional multimodal approaches by 3% and 5%. The study contributes to the development of accurate and efficient dysgraphia diagnosis tools, requiring only a single instance of multimodal word/pseudoword data. This work highlights the potential of multimodal learning in enhancing dysgraphia diagnosis. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Kids with developmental dysgraphia have trouble writing. Researchers want to use machine learning to help diagnose this problem. Right now, most studies look at online handwriting or offline handwriting separately. But what if we could combine both types of handwriting? This study does just that by creating a new dataset and using special machines learning algorithms. The result is a way to diagnose dysgraphia more accurately than before. It’s like having a superpower for helping kids with writing! |
Keywords
» Artificial intelligence » Machine learning » Xgboost
