A Data-Driven Fatigue Prediction using Recurrent Neural Networks

Lambay, Arsalan; Liu, Ying; Morgan, Phillip L.; Ji, Ze

doi:10.1109/hora52670.2021.9461377

Cited by 9 publications

(5 citation statements)

References 19 publications

(18 reference statements)

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“…Despite the high performance that machine learning models can reach, their real-life applicability remains limited if a comprehensive understanding of the underlying model self-arranged through the training algorithm is not extracted [55]. Consequently, despite their advantages, they have not been used in multilevel fatigue quantification approaches, being only included in binary fatigue prediction models (recurrent neural networks were used in [56]), mental fatigue (multilayer neural networks were applied in [57]), and drowsiness (convolutional neural networks were used in [58]) detection with accuracies around 70%, which is under the accuracy reached by other techniques [3]. Overcoming data and interpretability constraints, supervised learning models have been predominant for fatigue quantification models, with decision trees, support-vector machines and random forests being the most common [3,59].…”

Section: Discussionmentioning

confidence: 99%

Exploring the Applicability of Physiological Monitoring to Manage Physical Fatigue in Firefighters

Bustos

Cardoso

Carvalho

et al. 2023

Sensors

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Physical fatigue reduces productivity and quality of work while increasing the risk of injuries and accidents among safety-sensitive professionals. To prevent its adverse effects, researchers are developing automated assessment methods that, despite being highly accurate, require a comprehensive understanding of underlying mechanisms and variables’ contributions to determine their real-life applicability. This work aims to evaluate the performance variations of a previously developed four-level physical fatigue model when alternating its inputs to have a comprehensive view of the impact of each physiological variable on the model’s functioning. Data from heart rate, breathing rate, core temperature and personal characteristics from 24 firefighters during an incremental running protocol were used to develop the physical fatigue model based on an XGBoosted tree classifier. The model was trained 11 times with different input combinations resulting from alternating four groups of features. Performance measures from each case showed that heart rate is the most relevant signal for estimating physical fatigue. Breathing rate and core temperature enhanced the model when combined with heart rate but showed poor performance individually. Overall, this study highlights the advantage of using more than one physiological measure for improving physical fatigue modelling. The findings can contribute to variables and sensor selection in occupational applications and as the foundation for further field research.

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Section: Discussionmentioning

confidence: 99%

Exploring the Applicability of Physiological Monitoring to Manage Physical Fatigue in Firefighters

Bustos

Cardoso

Carvalho

et al. 2023

Sensors

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“…This methodology was similarly adopted in studies by Zhang et al, (2013), Baghdadi et al, (2018), and Kuschan and Krüger (2021), who also used SVM models for physical fatigue detection [41,42,43]. 2017) to conduct fatigue prediction for manual material handling tasks [44]. These studies collectively contribute to the development of a proactive approach to the continuous monitoring of operator's fatigue levels, with the potential to enhance work performance and mitigate the earlier-mentioned risks [45,46].…”

Section: Literature Reviewmentioning

confidence: 99%

Digital Measurement of Employee Stress and Fatigue in Artificial Intelligence-Driven Workforce Management Environments: An Empirical Study Using Aicoach

Adam Dalnoki,

Md Iftekhar Islam

2023

EPRA

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Effective fatigue management in the workplace is associated with employee well-being, safety, and productivity. Fatigue, often resulting from inadequate rest and high workloads, can lead to decreased cognitive function, diminished job performance, and increased risk of accidents, thus affecting both individual health and organizational efficiency. For this, we introduced an innovative approach to understanding and addressing workplace fatigue. By analyzing the interrelationships between employees self-reported fatigue levels, their perceptions of managing fatigue, and their engagement with an AI-based coaching tool (AiCoach), we sought to uncover patterns that could inform more effective fatigue management strategies. Using advanced sentiment analysis adapted for the context of fatigue and Granger-causality tests, we examined these dynamics over time. Our findings highlight the importance of immediate perceptions of fatigue in predicting engagement with management interventions, underscoring the need for real-time monitoring and adaptive strategies in managing workplace fatigue. KEYWORDS: Workplace Fatigue, Stress Management, AiCoach, Sentiment Analysis.

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“…The above studies are only some typical application cases. There are many successful application cases of RNN in the construction of the driving behavior recognition model [116][117][118][119][120][121][122][123][124]. It can be seen that RNN can be used for all kinds of driving behavior recognition, is suitable for highdimensional and big data sample learning, and can extract deep temporal and spatial features with an accuracy rate of more than 90%.…”

Section: Recurrent Neuralmentioning

confidence: 99%

A Review for the Driving Behavior Recognition Methods Based on Vehicle Multisensor Information

Zhao

Zhong

Zhi-jun

et al. 2022

Journal of Advanced Transportation

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The frequent traffic accidents lead to a large number of casualties and large related financial losses every year; this serious state is owed to several factors; among those, driving behavior is one of the most imperative subjects to discuss. Driving behaviors mainly include behavior characteristics such as car-following, lane change, and risky driving behavior such as distraction, fatigue, or aggressive driving, which are of great help to various tasks in traffic engineering. An accurate and reliable method of driving behavior recognition is of great significance and guidance for vehicle driving safety. In this paper, the vehicle multisensor information, vehicle CAN bus data acquisition system, and typical feature extraction methods are summarized at first. And then, several driving behavior recognition models based on machine learning and deep learning are reviewed. Through a detailed analysis of the features of random forests, support vector machines, convolutional neural networks, and recurrent neural networks used to build driving behavior recognition models, the following findings are obtained: the driving behavior model constructed by traditional machine learning model is relatively mature but it is greatly affected by feature extraction, data scale, and model structure, which affects the accuracy of the final driving behavior recognition. Deep learning model based on a neural network has achieved high accuracy in identifying driving behavior, and it may gradually become the mainstream of constructing the driving behavior model with the development of big data, artificial intelligence technology, and computer hardware. Finally, this paper points out some content that needs to be further explored, to provide reference and inspiration for scholars in this field to continue to study the driving behavior recognition model in depth.

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A Data-Driven Fatigue Prediction using Recurrent Neural Networks

Cited by 9 publications

References 19 publications

Exploring the Applicability of Physiological Monitoring to Manage Physical Fatigue in Firefighters

Exploring the Applicability of Physiological Monitoring to Manage Physical Fatigue in Firefighters

Digital Measurement of Employee Stress and Fatigue in Artificial Intelligence-Driven Workforce Management Environments: An Empirical Study Using Aicoach

A Review for the Driving Behavior Recognition Methods Based on Vehicle Multisensor Information

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