Summary of Ai Walkup: a Computer-vision Approach to Quantifying Mds-updrs in Parkinson’s Disease, by Xiang Xiang et al.
AI WALKUP: A Computer-Vision Approach to Quantifying MDS-UPDRS in Parkinson’s Disease
by Xiang Xiang, Zihan Zhang, Jing Ma, Yao Deng
First submitted to arxiv on: 2 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Image and Video Processing (eess.IV); Signal Processing (eess.SP)
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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 proposes a computer vision-based solution to assess Parkinson’s Disease (PD) severity and progression using smartphone cameras. The existing method, MDS-UPDRS, is subjective, inconsistent, and costly. The proposed approach captures human pose images, reconstructs motion, and extracts features through algorithms and feature engineering. This solution can be deployed on various smartphones, allowing for quick and easy video recording and AI analysis through an APP. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper uses computer vision to help diagnose Parkinson’s Disease. Doctors usually use a rating scale to check how bad the symptoms are, but this method is not very good because it relies on people’s opinions and takes a long time. The new approach uses cameras on smartphones to capture pictures of people moving, then uses special algorithms to analyze these images and measure how much movement there is. This makes it faster, cheaper, and more accurate than the current method. |
Keywords
» Artificial intelligence » Feature engineering
