Driver Fatigue Detection using Mean Intensity, SVM, and SIFT

Naz, Sadia; Ziauddin, Sheikh; Raza, Basit

doi:10.9781/ijimai.2017.10.002

Cited by 6 publications

(5 citation statements)

References 37 publications

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“…Another advantage of our fatigue estimation method is the usage of artificial intelligence and machine learning. Artificial intelligence has been widely used to detect fatigue among drivers [ 52 , 53 , 54 , 55 , 56 ], but it has seldom been used to detect student fatigue. Nevertheless, online education prevails around the world due to the COVID-19 pandemic.…”

Section: Discussionmentioning

confidence: 99%

An Explainable Student Fatigue Monitoring Module with Joint Facial Representation

Lin

Tian

et al. 2023

Sensors

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Online fatigue estimation is, inevitably, in demand as fatigue can impair the health of college students and lower the quality of higher education. Therefore, it is essential to monitor college students’ fatigue to diminish its adverse effects on the health and academic performance of college students. However, former studies on student fatigue monitoring are mainly survey-based with offline analysis, instead of using constant fatigue monitoring. Hence, we proposed an explainable student fatigue estimation model based on joint facial representation. This model includes two modules: a spacial–temporal symptom classification module and a data-experience joint status inferring module. The first module tracks a student’s face and generates spatial–temporal features using a deep convolutional neural network (CNN) for the relevant drivers of abnormal symptom classification; the second module infers a student’s status with symptom classification results with maximum a posteriori (MAP) under the data-experience joint constraints. The model was trained on the benchmark NTHU Driver Drowsiness Detection (NTHU-DDD) dataset and tested on an Online Student Fatigue Monitoring (OSFM) dataset. Our method outperformed the other methods with an accuracy rate of 94.47% under the same training–testing setting. The results were significant for real-time monitoring of students’ fatigue states during online classes and could also provide practical strategies for in-person education.

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Section: Discussionmentioning

confidence: 99%

An Explainable Student Fatigue Monitoring Module with Joint Facial Representation

Lin

Tian

et al. 2023

Sensors

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“…In fatigue detection, the fuzzy samples in the feature space often lead to reduction of the classification interval and affect the classification performance of the classifier [18]. In practical applications, training samples are often affected by some noise.…”

Section: Speech Feature Classification Algorithm Based On the Fsvmmentioning

confidence: 99%

Analyzing Multi-Mode Fatigue Information from Speech and Gaze Data from Air Traffic Controllers

Xu,

Ma,

Shen

et al. 2023

Aerospace

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In order to determine the fatigue state of air traffic controllers from air talk, an algorithm is proposed for discriminating the fatigue state of controllers based on applying multi-speech feature fusion to voice data using a Fuzzy Support Vector Machine (FSVM). To supplement the basis for discrimination, we also extracted eye-fatigue-state discrimination features based on Percentage of Eyelid Closure Duration (PERCLOS) eye data. To merge the two classes of discrimination results, a new controller fatigue-state evaluation index based on the entropy weight method is proposed, based on a decision-level fusion of fatigue discrimination results for speech and the eyes. The experimental results show that the fatigue-state recognition accuracy rate was 86.0% for the fatigue state evaluation index, which was 3.5% and 2.2%higher than those for speech and eye assessments, respectively. The comprehensive fatigue evaluation index provides important reference values for controller scheduling and mental-state evaluations.

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“…It is based on a notion that the higher the resultant acceleration, the more intensive the physical activity. A comparative analysis of actigraphy devices capable of tracking temperature, sleep and activity rhythms with minor discrepancies [28]. Actigraphy is used in Fatigue Risk Management systems for Airline operations as part of the protocol, to gather data used in biomathematical models of fatigue.…”

Section: )mentioning

confidence: 99%

Research of Human Fatigue and Measurement Parameters for Workability Assessment

Erins

Minejeva

Markovičs

et al. 2019

ETR

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Human fatigue is reduced working capability for certain period of time as the result of unusual or prolonged workload. Fatigue arises when the body’s energy requirements exceed its supply. Fatigue first manifests as reduced concentration capability causing movement coordination and precision disruption leading to decreasing workability. Fatigue is an object of research in physiology, psychology, work ergonomics, medicine, and biotechnology where each domain has a focus on mental fatigue. The functional state in the context of professional activity is defined as a complex of characteristics of functions and qualities that determine the fulfilment of labour activity. Furthermore, a comprehensive estimation of subject functional state in combination with other factors like subject self-assessment and objective performance tests (cognitive load tests) is a necessary input for the evaluation of workability and efficiency on task. The heterogeneous nature of fatigue as a systemic manifestation requires analysis of multiple key parameters which are relevant to the specific type. The current feasibility study focuses on human biological signal from electrical activity of heart, brain, muscles and skin potentials as well as temperature, position, and respiration to obtain diagnostic parameters reflecting the state of cardiovascular, muscles, and central nervous systems for physiological monitoring of vital signs. The fatigue physiological parameter and feature formalization aim to support the development of a platform with complex passive multi-level fatigue monitoring system and workability evaluation system designed in order to provide an integrated service.

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Driver Fatigue Detection using Mean Intensity, SVM, and SIFT

Cited by 6 publications

References 37 publications

An Explainable Student Fatigue Monitoring Module with Joint Facial Representation

An Explainable Student Fatigue Monitoring Module with Joint Facial Representation

Analyzing Multi-Mode Fatigue Information from Speech and Gaze Data from Air Traffic Controllers

Research of Human Fatigue and Measurement Parameters for Workability Assessment

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