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A comprehensive review of approaches to detect fatigue using machine learning techniques
Rohit Hooda1, Vedant Joshi2, Manan Shah3
1Gandhinagar Institute of Technology, Gujarat Technological University Gandhinagar Gujarat India.
Chronic Diseases and Translational Medicine
|May 27, 2022
Summary
This study explores fatigue detection methods, including machine learning (ML), to combat its growing prevalence. It compares various techniques to identify the most effective approaches for recognizing and mitigating fatigue.
Area of Science:
- Medical Sciences
- Computer Science
- Artificial Intelligence
Background:
- Technological advancements have spurred medical science breakthroughs.
- Increasingly rapid societal changes contribute to prevalent fatigue.
- Understanding and detecting fatigue is crucial in modern life.
Purpose of the Study:
- To define fatigue and its consequences.
- To explore machine learning (ML) approaches for fatigue detection.
- To compare various fatigue detection methods.
Main Methods:
- Categorization of fatigue detection methods into mathematical models, rule-based systems, ML, and deep learning.
- Comparative analysis of different algorithms for fatigue detection.
- Review of recent advancements in fatigue detection technologies.
Main Results:
- Identification of distinct categories for fatigue detection techniques.
- Comparison and contrast of various algorithms to determine promising approaches.
- Highlighting areas for future improvement in fatigue detection.
Conclusions:
- Machine learning offers promising avenues for fatigue detection.
- A comprehensive understanding of fatigue detection methods is presented.
- Further research is needed to refine and improve fatigue detection strategies.
