Hooda Rohit, Joshi Vedant, Shah Manan
Gandhinagar Institute of Technology, Gujarat Technological University Gandhinagar Gujarat India.
LJ Institute of Engineering and Technology, Gujarat Technological University Ahmedabad Gujarat India.
Chronic Dis Transl Med. 2022 Feb 24;8(1):26-35. doi: 10.1016/j.cdtm.2021.07.002. eCollection 2022 Mar.
In the past decades, there have been numerous advancements in the field of technology. This has led to many scientific breakthroughs in the field of medical sciences. In this, rapidly transforming world we are having a difficult time and the problem of fatigue is becoming prevalent. So, this study aimed to understand what is fatigue, its repercussions, and techniques to detect it using machine learning (ML) approaches. This paper introduces, discusses methods and recent advancements in the field of fatigue detection. Further, we categorized the methods that can be used to detect fatigue into four diverse groups, that is, mathematical models, rule-based implementation, ML, and deep learning. This study presents, compares, and contrasts various algorithms to find the most promising approach that can be used for the detection of fatigue. Finally, the paper discusses the possible areas for improvement.
在过去几十年里，技术领域取得了众多进展。这在医学领域带来了许多科学突破。在这个快速变化的世界里，我们正面临困难，疲劳问题日益普遍。因此，本研究旨在了解什么是疲劳、其影响以及使用机器学习（ML）方法检测疲劳的技术。本文介绍、讨论了疲劳检测领域的方法和最新进展。此外，我们将可用于检测疲劳的方法分为四类，即数学模型、基于规则的实现、机器学习和深度学习。本研究展示、比较并对比了各种算法，以找到最有前景的用于疲劳检测的方法。最后，本文讨论了可能的改进领域。
