Summary of Feasibility Of Assessing Cognitive Impairment Via Distributed Camera Network and Privacy-preserving Edge Computing, by Chaitra Hegde et al.
Feasibility of assessing cognitive impairment via distributed camera network and privacy-preserving edge computing
by Chaitra Hegde, Yashar Kiarashi, Allan I Levey, Amy D Rodriguez, Hyeokhyen Kwon, Gari D Clifford
First submitted to arxiv on: 19 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: 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 Machine learning researchers have long sought to develop accurate methods for detecting mild cognitive impairment (MCI), a condition characterized by a decline in cognitive functions beyond typical age and education-related expectations. In this paper, we investigate the potential benefits of automating the capture of behavioral patterns in individuals with MCI. Specifically, we focus on two key indicators: reduced social interactions and increased aimless movements. By leveraging machine learning models trained on relevant datasets and benchmarks, we demonstrate that our approach can enhance longitudinal monitoring of these behaviors. Our method relies on a combination of computer vision and machine learning techniques to identify and track behavioral patterns in individuals with MCI. We evaluate the effectiveness of our approach using established evaluation metrics and benchmarking datasets. The results suggest that our automated system can accurately detect changes in social interactions and aimless movements, providing valuable insights for clinical monitoring and diagnosis. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Mild cognitive impairment (MCI) is a condition where people struggle with memory, thinking, and problem-solving skills more than usual as they age. Researchers want to find better ways to track this condition so doctors can help patients earlier. One way to do this is by watching how people move and interact socially. In this paper, scientists tried to create a computer system that can automatically capture these behaviors. They used special cameras and machine learning algorithms to identify when someone with MCI is having trouble interacting with others or is moving aimlessly. This system could help doctors keep track of patients’ progress over time and diagnose MCI more accurately. |
Keywords
* Artificial intelligence * Machine learning
