Ubi-Fatigue: Toward Ubiquitous Fatigue Detection via Contactless Sensing

Zhang, Jian; Wu, Yuan; Chen, Yanjiao; Wang, Junkongshuai; Huang, Jinxing; Zhang, Qian

doi:10.1109/jiot.2022.3146942

Cited by 16 publications

(6 citation statements)

References 34 publications

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“…MCSVM [36] 87.3% RF-DCM [37] 94.6% Drowsiness detection system [28] 85% Fatigue-Radio [33] 81.4% FFD system [31] 97.8% MTCNN + InceptionV3 [10] 91.1% HDDD [38] 88.9% CNN + DF-LSTM [39] 88.9% Fusion system [32] 97.3% Ours…”

Section: Methods Acc (%)mentioning

confidence: 99%

“…Acc = TP TP + FP + FN (33) where true positive (TP) represents the number of correctly detected fatigue states or nonfatigue states, false positive (FP) represents the number of non-fatigue states judged as fatigue states, and false negative (FN) indicates the number of fatigue states judged as non-fatigue.…”

Section: Model Evaluationmentioning

confidence: 99%

“…Using an infrared camera could be a potential solution. Zhang [ 33 ] introduced Ubi-Fatigue, a non-contact fatigue monitoring system combining vital signs and facial features to achieve reliable fatigue detection. The results demonstrated that Fatigue-Radio’s detection accuracy reached 81.4%, surpassing ECG or visual fatigue detection systems.…”

Section: Introductionmentioning

confidence: 99%

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Remote Photoplethysmography and Motion Tracking Convolutional Neural Network with Bidirectional Long Short-Term Memory: Non-Invasive Fatigue Detection Method Based on Multi-Modal Fusion

Kong,

Xie,

Niu

et al. 2024

Sensors

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Existing vision-based fatigue detection methods commonly utilize RGB cameras to extract facial and physiological features for monitoring driver fatigue. These features often include single indicators such as eyelid movement, yawning frequency, and heart rate. However, the accuracy of RGB cameras can be affected by factors like varying lighting conditions and motion. To address these challenges, we propose a non-invasive method for multi-modal fusion fatigue detection called RPPMT-CNN-BiLSTM. This method incorporates a feature extraction enhancement module based on the improved Pan–Tompkins algorithm and 1D-MTCNN. This enhances the accuracy of heart rate signal extraction and eyelid features. Furthermore, we use one-dimensional neural networks to construct two models based on heart rate and PERCLOS values, forming a fatigue detection model. To enhance the robustness and accuracy of fatigue detection, the trained model data results are input into the BiLSTM network. This generates a time-fitting relationship between the data extracted from the CNN, allowing for effective dynamic modeling and achieving multi-modal fusion fatigue detection. Numerous experiments validate the effectiveness of the proposed method, achieving an accuracy of 98.2% on the self-made MDAD (Multi-Modal Driver Alertness Dataset). This underscores the feasibility of the algorithm. In comparison with traditional methods, our approach demonstrates higher accuracy and positively contributes to maintaining traffic safety, thereby advancing the field of smart transportation.

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Section: Methods Acc (%)mentioning

confidence: 99%

Section: Model Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Remote Photoplethysmography and Motion Tracking Convolutional Neural Network with Bidirectional Long Short-Term Memory: Non-Invasive Fatigue Detection Method Based on Multi-Modal Fusion

Kong,

Xie,

Niu

et al. 2024

Sensors

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“…Fatigue can be observed by looking at motion, electroencephalogram (EEG), electrocardiogram (ECG) and respiratory data [23]. The work of [24] looks at observing the ECG and facial features to detect fatigue using a contactless system. The contactless system is an off-the-shelf radar system with ECG and a camera used for ground truth.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Sensing for Fatigue Detection Using Wearables and RF

Taylor,

Hill,

Adam

et al. 2024

IEEE Sensors J.

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In the field of smart healthcare, wearable and sensing devices are connected to the Internet of Things (IoT) to assess patients within their own homes. Fatigue is a multidimensional experience that can be characterised by exhaustion and reduced physical performance. Monitoring people within their homes to detect slower pace could be a promising method to objectively measure aspects of human fatigue. This paper makes use of both a wearable bracelet and Radio Frequency (RF) sensing to detect simulated fatigue from human activity monitoring. Different activities are collected at a normal pace to represent no fatigue and then repeated at a slower pace to represent fatigue. Artificial intelligence (AI) is used to detect if there is fatigue present or not regardless of activity taking place as well as identifying which activity took place and if fatigue is present in said activity. When using the data from both the bracelet and RF sensing, Random Forest and ResNet algorithms achieved 100 % in detecting fatigue as opposed to non-fatigue using algorithms. When using only the bracelet, only the Random Forest algorithm was able to achieve 100 % accuracy. Using only RF data, 94.80 % accuracy was achieved with a Convolutional Neural Network (CNN). When detecting individual activities with fatigue and no fatigue, the Random Forest algorithm achieved an accuracy score of 97.40 % using both the bracelet and RF sensing and with only the bracelet data. CNN was again the best algorithm for RF sensing only with an accuracy score of 89.84 %.

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“…Measuring driver fatigue is an essential issue because it has several processes [9]. Fatigue detection in previous studies has been developed using techniques for measuring brain waves, heart rate, and pulse [10], [11]. However, these techniques are considered disturbing because the driver must attach the worn device while driving.…”

Section: Introductionmentioning

confidence: 99%

Fatigue Detection Through Car Driver’s Face Using Boosting Local Binary Patterns

Setyo Utomo,

Rachmawati,

Sthevanie

2023

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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The general population is concerned with traffic accidents. Driver fatigue is one of the leading causes of car accidents. Several factors, including nighttime driving, sleep deprivation, alcohol consumption, driving on monotonous roads, and drowsy and fatigue-inducing drugs, can contribute to fatigue. This study proposes a facial appearance-based driver fatigue detection system. This is based on the assumption that facial features can be used to identify driver fatigue. We categorize driver conditions into three groups: normal, talking, and yawning. In this study, we used Adaboost to propose Boosting Local Binary Patterns (LBP) to improve the image features of fatigue drivers in the Support Vector Machine (SVM) model. The experimental results indicate that the system's optimal performance achieves an accuracy value of 93.68%, a recall value of 94%, and a precision value of 94%.

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Ubi-Fatigue: Toward Ubiquitous Fatigue Detection via Contactless Sensing

Cited by 16 publications

References 34 publications

Remote Photoplethysmography and Motion Tracking Convolutional Neural Network with Bidirectional Long Short-Term Memory: Non-Invasive Fatigue Detection Method Based on Multi-Modal Fusion

Remote Photoplethysmography and Motion Tracking Convolutional Neural Network with Bidirectional Long Short-Term Memory: Non-Invasive Fatigue Detection Method Based on Multi-Modal Fusion

Hybrid Sensing for Fatigue Detection Using Wearables and RF

Fatigue Detection Through Car Driver’s Face Using Boosting Local Binary Patterns

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