A Decentralized Sensor Fusion Approach to Human Fatigue Monitoring in Maritime Operations

Ana, Mateus Sant’; Li, Guoyuan; Zhang, Houxiang

doi:10.1109/icca.2019.8899708

Cited by 4 publications

(2 citation statements)

References 15 publications

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“…Due to advancements in camera technology in combination with novel approaches in computer vision and image processing, camera-based drowsiness detection has been receiving more and more attention in recent years [18]. These methods evaluate mainly three parameters: eye movements (eye blinking and eye closure activity) via eye-tracking, that was also investigated for usage in maritime operations and aviation [19][20][21], facial expressions (yawning, jaw drop, brow rise, and lip stretch), and head position (head scaling/nodding) [22]. In particular, many studies focused on the use of machine (deep) learning-based approaches [23][24][25][26][27].…”

Section: Driver Drowsiness Measurement Technologiesmentioning

confidence: 99%

Assessment of the Potential of Wrist-Worn Wearable Sensors for Driver Drowsiness Detection

Kundinger

Sofra

Riener

2020

Sensors

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Drowsy driving imposes a high safety risk. Current systems often use driving behavior parameters for driver drowsiness detection. The continuous driving automation reduces the availability of these parameters, therefore reducing the scope of such methods. Especially, techniques that include physiological measurements seem to be a promising alternative. However, in a dynamic environment such as driving, only non- or minimal intrusive methods are accepted, and vibrations from the roadbed could lead to degraded sensor technology. This work contributes to driver drowsiness detection with a machine learning approach applied solely to physiological data collected from a non-intrusive retrofittable system in the form of a wrist-worn wearable sensor. To check accuracy and feasibility, results are compared with reference data from a medical-grade ECG device. A user study with 30 participants in a high-fidelity driving simulator was conducted. Several machine learning algorithms for binary classification were applied in user-dependent and independent tests. Results provide evidence that the non-intrusive setting achieves a similar accuracy as compared to the medical-grade device, and high accuracies (>92%) could be achieved, especially in a user-dependent scenario. The proposed approach offers new possibilities for human–machine interaction in a car and especially for driver state monitoring in the field of automated driving.

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Section: Driver Drowsiness Measurement Technologiesmentioning

confidence: 99%

Assessment of the Potential of Wrist-Worn Wearable Sensors for Driver Drowsiness Detection

Kundinger

Sofra

Riener

2020

Sensors

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“…These features include min, max, mean, median, SD, variance, kurtosis, and RMS. Besides the eight features mentioned before, we also select three other features often associated with fatigue for better performance: skewness, IoP, and MSP [76][77][78][79]. A previous study suggests considering the skewness of the data when detecting fatigue in repetitive muscle movements such as bicep curls [55].…”

Section: Feature Extractionmentioning

confidence: 99%

Toward the Personalization of Biceps Fatigue Detection Model for Gym Activity: An Approach to Utilize Wearables’ Data from the Crowd

Elshafei

Costa

Shihab

2022

Sensors

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Nowadays, wearables-based Human Activity Recognition (HAR) systems represent a modern, robust, and lightweight solution to monitor athlete performance. However, user data variability is a problem that may hinder the performance of HAR systems, especially the cross-subject HAR models. Such a problem may have a lesser effect on the subject-specific model because it is a tailored model that serves a specific user; hence, data variability is usually low, and performance is often high. However, such a performance comes with a high cost in data collection and processing per user. Therefore, in this work, we present a personalized model that achieves higher performance than the cross-subject model while maintaining a lower data cost than the subject-specific model. Our personalization approach sources data from the crowd based on similarity scores computed between the test subject and the individuals in the crowd. Our dataset consists of 3750 concentration curl repetitions from 25 volunteers with ages and BMI ranging between 20–46 and 24–46, respectively. We compute 11 hand-crafted features and train 2 personalized AdaBoost models, Decision Tree (AdaBoost-DT) and Artificial Neural Networks (AdaBoost-ANN), using data from whom the test subject shares similar physical and single traits. Our findings show that the AdaBoost-DT model outperforms the cross-subject-DT model by 5.89%, while the AdaBoost-ANN model outperforms the cross-subject-ANN model by 3.38%. On the other hand, at 50.0% less of the test subject’s data consumption, our AdaBoost-DT model outperforms the subject-specific-DT model by 16%, while the AdaBoost-ANN model outperforms the subject-specific-ANN model by 10.33%. Yet, the subject-specific models achieve the best performances at 100% of the test subjects’ data consumption.

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Mental Fatigue Assessment Sensor Framework

Monteiro,

Zhang

2024

Mental Fatigue Assessment in Demanding Marine Operations

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A Decentralized Sensor Fusion Approach to Human Fatigue Monitoring in Maritime Operations

Cited by 4 publications

References 15 publications

Assessment of the Potential of Wrist-Worn Wearable Sensors for Driver Drowsiness Detection

Assessment of the Potential of Wrist-Worn Wearable Sensors for Driver Drowsiness Detection

Toward the Personalization of Biceps Fatigue Detection Model for Gym Activity: An Approach to Utilize Wearables’ Data from the Crowd

Mental Fatigue Assessment Sensor Framework

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