Summary of Benchmarking Reliability Of Deep Learning Models For Pathological Gait Classification, by Abhishek Jaiswal and Nisheeth Srivastava
Benchmarking Reliability of Deep Learning Models for Pathological Gait Classification
by Abhishek Jaiswal, Nisheeth Srivastava
First submitted to arxiv on: 20 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 This paper tackles the challenge of early detection of neurodegenerative disorders, aiming to leverage machine learning algorithms for accurate diagnosis and treatment. Despite recent claims of successful detection methods using various sensors and algorithms, there is a significant gap between these approaches and their practical implementation. The authors analyze existing solutions, identifying errors and generalization failures in three Kinect-simulated and one real Parkinson’s patient dataset. Based on these findings, they propose the Asynchronous Multi-Stream Graph Convolutional Network (AMS-GCN) as a strong baseline for reliably distinguishing multiple categories of pathological gaits across datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding a way to quickly detect signs that someone might have a brain disease. It’s important because if we can catch these diseases early, we can treat them better and help people live longer. Some researchers have tried using special machines and computer programs to recognize changes in how people walk, which could be a sign of a disease. However, the methods they used didn’t quite work as promised. The authors looked at what went wrong with these methods and came up with their own way to tell different types of bad walking patterns apart. |
Keywords
» Artificial intelligence » Convolutional network » Gcn » Generalization » Machine learning
