Summary of A Survey Of Artificial Intelligence in Gait-based Neurodegenerative Disease Diagnosis, by Haocong Rao et al.
A Survey of Artificial Intelligence in Gait-Based Neurodegenerative Disease Diagnosis
by Haocong Rao, Minlin Zeng, Xuejiao Zhao, Chunyan Miao
First submitted to arxiv on: 21 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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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 A comprehensive survey on machine learning and deep learning-based artificial intelligence (AI) techniques is provided to diagnose neurodegenerative diseases (NDs) through gait analysis. The paper overviews the process of AI-assisted NDs diagnosis, presents a systematic taxonomy of existing gait data and AI models, and proposes a novel quality evaluation criterion for assessing study quality. An extensive review of 169 studies reveals recent technical advancements, discusses existing challenges, potential solutions, and future directions in this field. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Artificial intelligence can help diagnose neurodegenerative diseases like Alzheimer’s or Parkinson’s by analyzing how people walk. This paper looks at the latest AI techniques being used to do this. It shows what kinds of data are needed to make these diagnoses and what kind of AI models work best. The researchers also came up with a way to check if studies in this field are good or not. They looked at 169 different studies and found out what’s working well, what’s not, and where things need to improve. |
Keywords
» Artificial intelligence » Deep learning » Machine learning
