A comprehensive review of approaches to detect fatigue using machine learning techniques, 2021, Hooda

https://www.sciencedirect.com/science/article/pii/S2095882X21000487

Chronic Diseases and Translational Medicine
Available online 25 August 2021

Review
A comprehensive review of approaches to detect fatigue using machine learning techniques

Rohit Hoodaa Vedant Joshib MananShahc
https://doi.org/10.1016/j.cdtm.2021.07.002

open access

Abstract


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, i.e., 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.

Keywords
Fatigue detection
Machine learning
Deep learning
Driver monitoring
Healthcare

 

This discusses objective measures for measuring fatigue

 

Yawning being there suggests a typical misunderstanding of fatigue as sleepiness.

 

Since yawns are contagious, does that mean that fatigue is?

I'm surprised that they didn't use muscle movements or posture as features. I'd expect frequency and speed of movements to decrease with fatigue.

My question is: how did they determine the accuracy of their fatigue quantification when there's no known way of accurately measuring fatigue to compare it to?