Review of the article Evaluation of Fatigue Level at Safe Entry Station Using Novel Deep Learning Technique, verified by Publons, Web of Science
Abstract
Fatigue is the initial checkpoint to look into Fit for Duty at work place. The main objective of this research paper is to acquire an understanding of fatigue of individuals in organizations. In order to achieve personal and professional goals, enhance the structure of the organization and to sustain one’s living conditions in an appropriate manner, it is necessary to take into consideration the aspects of health and safety of the employees. This research paper presents our views on the importance of detecting health, safety and fatigue in the workplace at a preliminary stage with state-of-art technology before even starting a job.
This paper proposes a real-time comprehensive employee fatigue detection algorithm based on different facial landmarks to improve the detection accuracy, which detects the employee’s fatigue status by using facial video sequences without equipping them with sensor devices. The purpose of using the face area so it can narrow down to detect eyes and mouth wit...hin the face area. Once the face is found, the eyes and mouth are found by creating the eye for left and right eye detection and also mouth detection.
Level of fatigue is very important to evaluate the level of efficiency at work. In this paper we are proposing a novel deep learning technique to classify high, mid and low levels of fatigue. We are performing this activity at a safe entry station(SES). Overall objective is to measure other vital signs parameters such as Body Temperature, Eye Redness, Heart Rate, Respiration Rate at SES. In this paper we are focusing on Fatigue detection only. Our AI pipeline achieved 91% accuracy on data points collected at various sites.
Source:
Journal of Basic and Applied Research International, 2022, 1/Ms_JOBARI_11173-11URI
httos://www.webofscience.com/wos/author/record/1672007https://machinery.mas.bg.ac.rs/handle/123456789/5819
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Mašinski fakultetTY - JOUR PY - 2022 UR - httos://www.webofscience.com/wos/author/record/1672007 UR - https://machinery.mas.bg.ac.rs/handle/123456789/5819 AB - Fatigue is the initial checkpoint to look into Fit for Duty at work place. The main objective of this research paper is to acquire an understanding of fatigue of individuals in organizations. In order to achieve personal and professional goals, enhance the structure of the organization and to sustain one’s living conditions in an appropriate manner, it is necessary to take into consideration the aspects of health and safety of the employees. This research paper presents our views on the importance of detecting health, safety and fatigue in the workplace at a preliminary stage with state-of-art technology before even starting a job. This paper proposes a real-time comprehensive employee fatigue detection algorithm based on different facial landmarks to improve the detection accuracy, which detects the employee’s fatigue status by using facial video sequences without equipping them with sensor devices. The purpose of using the face area so it can narrow down to detect eyes and mouth within the face area. Once the face is found, the eyes and mouth are found by creating the eye for left and right eye detection and also mouth detection. Level of fatigue is very important to evaluate the level of efficiency at work. In this paper we are proposing a novel deep learning technique to classify high, mid and low levels of fatigue. We are performing this activity at a safe entry station(SES). Overall objective is to measure other vital signs parameters such as Body Temperature, Eye Redness, Heart Rate, Respiration Rate at SES. In this paper we are focusing on Fatigue detection only. Our AI pipeline achieved 91% accuracy on data points collected at various sites. T2 - Journal of Basic and Applied Research International T1 - Review of the article Evaluation of Fatigue Level at Safe Entry Station Using Novel Deep Learning Technique, verified by Publons, Web of Science EP - 11 SP - 1/Ms_JOBARI_11173 UR - https://hdl.handle.net/21.15107/rcub_machinery_5819 ER -
@article{ year = "2022", abstract = "Fatigue is the initial checkpoint to look into Fit for Duty at work place. The main objective of this research paper is to acquire an understanding of fatigue of individuals in organizations. In order to achieve personal and professional goals, enhance the structure of the organization and to sustain one’s living conditions in an appropriate manner, it is necessary to take into consideration the aspects of health and safety of the employees. This research paper presents our views on the importance of detecting health, safety and fatigue in the workplace at a preliminary stage with state-of-art technology before even starting a job. This paper proposes a real-time comprehensive employee fatigue detection algorithm based on different facial landmarks to improve the detection accuracy, which detects the employee’s fatigue status by using facial video sequences without equipping them with sensor devices. The purpose of using the face area so it can narrow down to detect eyes and mouth within the face area. Once the face is found, the eyes and mouth are found by creating the eye for left and right eye detection and also mouth detection. Level of fatigue is very important to evaluate the level of efficiency at work. In this paper we are proposing a novel deep learning technique to classify high, mid and low levels of fatigue. We are performing this activity at a safe entry station(SES). Overall objective is to measure other vital signs parameters such as Body Temperature, Eye Redness, Heart Rate, Respiration Rate at SES. In this paper we are focusing on Fatigue detection only. Our AI pipeline achieved 91% accuracy on data points collected at various sites.", journal = "Journal of Basic and Applied Research International", title = "Review of the article Evaluation of Fatigue Level at Safe Entry Station Using Novel Deep Learning Technique, verified by Publons, Web of Science", pages = "11-1/Ms_JOBARI_11173", url = "https://hdl.handle.net/21.15107/rcub_machinery_5819" }
(2022). Review of the article Evaluation of Fatigue Level at Safe Entry Station Using Novel Deep Learning Technique, verified by Publons, Web of Science. in Journal of Basic and Applied Research International, 1/Ms_JOBARI_11173-11. https://hdl.handle.net/21.15107/rcub_machinery_5819
Review of the article Evaluation of Fatigue Level at Safe Entry Station Using Novel Deep Learning Technique, verified by Publons, Web of Science. in Journal of Basic and Applied Research International. 2022;:1/Ms_JOBARI_11173-11. https://hdl.handle.net/21.15107/rcub_machinery_5819 .
"Review of the article Evaluation of Fatigue Level at Safe Entry Station Using Novel Deep Learning Technique, verified by Publons, Web of Science" in Journal of Basic and Applied Research International (2022):1/Ms_JOBARI_11173-11, https://hdl.handle.net/21.15107/rcub_machinery_5819 .