Summary of Your Turn: at Home Turning Angle Estimation For Parkinson’s Disease Severity Assessment, by Qiushuo Cheng et al.
Your Turn: At Home Turning Angle Estimation for Parkinson’s Disease Severity Assessment
by Qiushuo Cheng, Catherine Morgan, Arindam Sikdar, Alessandro Masullo, Alan Whone, Majid Mirmehdi
First submitted to arxiv on: 15 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 researchers have developed a deep learning-based approach to automatically quantify turning angles in people with Parkinson’s Disease (PD). The method uses 3D skeletons from videos and calculates the rotation of hip and knee joints. This technique has the potential to measure PD symptoms continuously and passively, allowing for more accurate tracking of disease progression. The study utilizes state-of-the-art human pose estimation models on a dataset of unscripted free-living videos in a home-like setting. The results show that the method achieves an accuracy of 41.6%, Mean Absolute Error (MAE) of 34.7°, and weighted precision WPrec of 68.3%. This work has important implications for PD research and treatment. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary People with Parkinson’s Disease often have trouble turning because of the disease. Current methods don’t capture how symptoms change from hour to hour, only how they are during short visits to the doctor. Researchers wanted to find a way to measure turns more accurately. They used videos taken in people’s homes and special computer models that can track movements. The goal is to use this method to track changes in people with PD over time. |
Keywords
» Artificial intelligence » Deep learning » Machine learning » Mae » Pose estimation » Precision » Tracking
