Study on a Portable Electrode Used to Detect the Fatigue of Tower Crane Drivers in Real Construction Environment

Wang, Fuwang; Ma, Mingjia; Zhang, Xiaolei

doi:10.1109/tim.2024.3353274

Cited by 10 publications

(4 citation statements)

References 59 publications

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“…Although recent machine learning and deep learning methodologies have contributed to accurately classifying EEG signals [ 77 , 78 , 79 ], we intentionally avoided them in our research for several reasons. Firstly, our study segmented EEG data based on visual-based episodes.…”

Section: Discussionmentioning

confidence: 99%

Association of Visual-Based Signals with Electroencephalography Patterns in Enhancing the Drowsiness Detection in Drivers with Obstructive Sleep Apnea

Minhas,

Peker,

Hakkoz

et al. 2024

Sensors

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Individuals with obstructive sleep apnea (OSA) face increased accident risks due to excessive daytime sleepiness. PERCLOS, a recognized drowsiness detection method, encounters challenges from image quality, eyewear interference, and lighting variations, impacting its performance, and requiring validation through physiological signals. We propose visual-based scoring using adaptive thresholding for eye aspect ratio with OpenCV for face detection and Dlib for eye detection from video recordings. This technique identified 453 drowsiness (PERCLOS ≥ 0.3 || CLOSDUR ≥ 2 s) and 474 wakefulness episodes (PERCLOS < 0.3 and CLOSDUR < 2 s) among fifty OSA drivers in a 50 min driving simulation while wearing six-channel EEG electrodes. Applying discrete wavelet transform, we derived ten EEG features, correlated them with visual-based episodes using various criteria, and assessed the sensitivity of brain regions and individual EEG channels. Among these features, theta–alpha-ratio exhibited robust mapping (94.7%) with visual-based scoring, followed by delta–alpha-ratio (87.2%) and delta–theta-ratio (86.7%). Frontal area (86.4%) and channel F4 (75.4%) aligned most episodes with theta–alpha-ratio, while frontal, and occipital regions, particularly channels F4 and O2, displayed superior alignment across multiple features. Adding frontal or occipital channels could correlate all episodes with EEG patterns, reducing hardware needs. Our work could potentially enhance real-time drowsiness detection reliability and assess fitness to drive in OSA drivers.

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Section: Discussionmentioning

confidence: 99%

Association of Visual-Based Signals with Electroencephalography Patterns in Enhancing the Drowsiness Detection in Drivers with Obstructive Sleep Apnea

Minhas,

Peker,

Hakkoz

et al. 2024

Sensors

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“…Two important insights are as follows: (1) Powerful convolutional neural networks (CNNs) can be used to find and segment objects from the ground-up area predictions; (2) When tagged training data are unavailable, pre-supervised data can be used to considerably increase performance through auxiliary task training and subsequent domain-specific fine-tuning. In region classification, accurately locating boundaries between different semantic regions in images can be challenging Wang et al (2024) , especially when objects overlap or are closely positioned. This can lead to less precise segmentation results as the method may struggle to assign accurate semantic labels to distinct regions.…”

Section: Literature Reviewmentioning

confidence: 99%

“… Seong et al (2020) used CNN to create a novel scene identification approach. Wang et al, 2024 The proposed technique leverages the CNN framework, FOS Net (fusion of objects and scenario), based on the fusion of object semantics and deep appearance features for scene recognition and scene information in the provided image. Moreover, to train the FOSNet and improve scene identification performance, a unique loss called scene consistency loss (SCL) is being developed.…”

Section: Literature Reviewmentioning

confidence: 99%

Remote intelligent perception system for multi-object detection

Alazeb,

Chughtai,

Al Mudawi

et al. 2024

Front. Neurorobot.

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IntroductionDuring the last few years, a heightened interest has been shown in classifying scene images depicting diverse robotic environments. The surge in interest can be attributed to significant improvements in visual sensor technology, which has enhanced image analysis capabilities.MethodsAdvances in vision technology have a major impact on the areas of multiple object detection and scene understanding. These tasks are an integral part of a variety of technologies, including integrating scenes in augmented reality, facilitating robot navigation, enabling autonomous driving systems, and improving applications in tourist information. Despite significant strides in visual interpretation, numerous challenges persist, encompassing semantic understanding, occlusion, orientation, insufficient availability of labeled data, uneven illumination including shadows and lighting, variation in direction, and object size and changing background. To overcome these challenges, we proposed an innovative scene recognition framework, which proved to be highly effective and yielded remarkable results. First, we perform preprocessing using kernel convolution on scene data. Second, we perform semantic segmentation using UNet segmentation. Then, we extract features from these segmented data using discrete wavelet transform (DWT), Sobel and Laplacian, and textual (local binary pattern analysis). To recognize the object, we have used deep belief network and then find the object-to-object relation. Finally, AlexNet is used to assign the relevant labels to the scene based on recognized objects in the image.ResultsThe performance of the proposed system was validated using three standard datasets: PASCALVOC-12, Cityscapes, and Caltech 101. The accuracy attained on the PASCALVOC-12 dataset exceeds 96% while achieving a rate of 95.90% on the Cityscapes dataset.DiscussionFurthermore, the model demonstrates a commendable accuracy of 92.2% on the Caltech 101 dataset. This model showcases noteworthy advancements beyond the capabilities of current models.

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“…After that, features were extracted. The Variance threshold feature selection method [16,17] is used for feature selection, the selected feature vectors are balanced using the data augmentation technique, and after that, the augmented data are well optimized before classification using the Yeo-Johnson power transformation technique. Finally, physical and localization activity classification is performed by a multi-layer perceptron (MLP).…”

Section: Introductionmentioning

confidence: 99%

Biosensor-Driven IoT Wearables for Accurate Body Motion Tracking and Localization

Almujally,

Khan,

Al Mudawi

et al. 2024

Sensors

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The domain of human locomotion identification through smartphone sensors is witnessing rapid expansion within the realm of research. This domain boasts significant potential across various sectors, including healthcare, sports, security systems, home automation, and real-time location tracking. Despite the considerable volume of existing research, the greater portion of it has primarily concentrated on locomotion activities. Comparatively less emphasis has been placed on the recognition of human localization patterns. In the current study, we introduce a system by facilitating the recognition of both human physical and location-based patterns. This system utilizes the capabilities of smartphone sensors to achieve its objectives. Our goal is to develop a system that can accurately identify different human physical and localization activities, such as walking, running, jumping, indoor, and outdoor activities. To achieve this, we perform preprocessing on the raw sensor data using a Butterworth filter for inertial sensors and a Median Filter for Global Positioning System (GPS) and then applying Hamming windowing techniques to segment the filtered data. We then extract features from the raw inertial and GPS sensors and select relevant features using the variance threshold feature selection method. The extrasensory dataset exhibits an imbalanced number of samples for certain activities. To address this issue, the permutation-based data augmentation technique is employed. The augmented features are optimized using the Yeo–Johnson power transformation algorithm before being sent to a multi-layer perceptron for classification. We evaluate our system using the K-fold cross-validation technique. The datasets used in this study are the Extrasensory and Sussex Huawei Locomotion (SHL), which contain both physical and localization activities. Our experiments demonstrate that our system achieves high accuracy with 96% and 94% over Extrasensory and SHL in physical activities and 94% and 91% over Extrasensory and SHL in the location-based activities, outperforming previous state-of-the-art methods in recognizing both types of activities.

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Study on a Portable Electrode Used to Detect the Fatigue of Tower Crane Drivers in Real Construction Environment

Cited by 10 publications

References 59 publications

Association of Visual-Based Signals with Electroencephalography Patterns in Enhancing the Drowsiness Detection in Drivers with Obstructive Sleep Apnea

Association of Visual-Based Signals with Electroencephalography Patterns in Enhancing the Drowsiness Detection in Drivers with Obstructive Sleep Apnea

Remote intelligent perception system for multi-object detection

Biosensor-Driven IoT Wearables for Accurate Body Motion Tracking and Localization

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