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Abstract The construction industry substantially contributes to the economic growth of a country. However, it records a large number of workplace injuries and fatalities annually due to its hesitant adoption of automated safety monitoring systems. To address this critical concern, this study presents a real-time monitoring approach that uses the Internet of Things and ensemble learning. This study leverages wearable sensor technology, such as photoplethysmography and electroencephalography sensors, to continuously track the physiological parameters of construction workers. The sensor data is processed using an ensemble learning approach called the ChronoEnsemble Fatigue Analysis System (CEFAS), comprising deep autoregressive and temporal fusion transformer models, to accurately predict potential physical and mental fatigue. Comprehensive evaluation metrics, including mean square error, mean absolute scaled error, and symmetric mean absolute percentage error, demonstrated the superior prediction accuracy and reliability of the proposed model compared to standalone models. The ensemble learning model exhibited remarkable precision in predicting physical and mental fatigue, as evidenced by the mean square errors of 0.0008 and 0.0033, respectively. The proposed model promptly recognizes potential hazards and irregularities, considerably enhancing worker safety and reducing on-site risks.

This paper introduces and implements a method to monitor muscle fatigue in real-time using a wearable biofeedback system to improve muscle rehabilitation treatments. The biofeedback system consists of an electromyography (EMG) sensor to capture muscle activity and two motion sensors to track knee angles. The proposed method for monitoring muscle fatigue involves three steps: (1) recognition of the movement phases during the knee extension exercise; (2) clipping of the EMG signal and calculation of fatigue-related metrics; and (3) normalization of metrics through a calibration process. An experimental session was performed with 10 healthy subjects performing 50 repetitions of the knee extension exercise. Processed data revealed changes in fatigue-related metrics, which align with existing literature. A comparison was also made between real-time and computer processing using raw data. While minor differences were noted between the two processing methods, the mobile app closely mirrored the trajectory of processed data in the cloud, ensuring reliability and consistency. This study advances remote muscle rehabilitation by quantifying muscle fatigue during treatment sessions. Thus, health professionals can tailor treatment plans based on individual patient characteristics, optimizing treatment duration, and reducing injury risk. [ABSTRACT FROM AUTHOR]

This study refines the YOLOv7-tiny model through structured pruning and architectural fine-tuning, specifically for real-time eye state detection. By focusing on enhancing the model’s efficiency, particularly in environments with limited computational resources, this research contributes significantly to advancing driver monitoring systems, where timely and accurate detection of eye states such as openness or closure can prevent accidents caused by drowsiness or inattention. Structured pruning was utilized to simplify the YOLOv7-tiny model, reducing complexity and storage requirements. Subsequent fine-tuning involved adjustments to the model’s width and depth to further enhance processing speed and efficiency. The experimental outcomes reveal a pronounced reduction in storage size, of approximately 97%, accompanied by a sixfold increase in frames per second (FPS). Despite these substantial modifications, the model sustains high levels of precision, recall, and mean average precision (mAP). These improvements indicate a significant enhancement in both the speed and efficiency of the model, rendering it highly suitable for real-time applications where computational resources are limited.

Analyzing the opening and closing states of eyes and mouths by detecting the driver’s face feature points is an effective method for judging driver fatigue. However, in practical engineering applications, with the expansion of user groups, the false identification problem caused by the differentiation of individual facial features of drivers is prominent, especially for people with small eyes. To solve this problem, this paper uses the Mediapipe Facemesh module to detect face feature points and designs a perception-free calibration method for setting personalized eye opening and closing threshold combined with head postures. Compared with the traditional method of setting a fixed threshold, the precision of eye state recognition is improved by 36.4%. Finally, the model deployment and post-processing compilation are completed on the Xavier vehicle chip, achieving a running speed of 34 frames per second at most, and the subjective evaluation experience of the fatigue monitoring system is significantly improved.

Nighttime driving presents a critical challenge to road safety due to insufficient lighting and increased risk of driver fatigue. Existing methods for monitoring driver fatigue, mainly focusing on behavioral analysis and biometric monitoring, face significant challenges under low-light conditions. Their effectiveness, especially in dynamic lighting environments, is limited by their dependency on specific environmental conditions and active driver participation, leading to reduced accuracy and practicality in real-world scenarios. This study introduces a novel 'Illumination Intelligent Adaptation and Analysis Framework (IIAAF)', aimed at addressing these limitations and enhancing the accuracy and practicality of driver fatigue monitoring under nighttime low-light conditions. The IIAAF framework employs a multidimensional technology integration, including comprehensive body posture analysis and facial fatigue feature detection, per-pixel dynamic illumination adjustment technology, and a light variation feature learning system based on Convolutional Neural Networks (CNN) and time-series analysis. Through this integrated approach, the framework is capable of accurately capturing subtle fatigue signals in nighttime driving environments and adapting in real-time to rapid changes in lighting conditions. Experimental results on two independent datasets indicate that the IIAAF framework significantly improves the accuracy of fatigue detection under nighttime low-light conditions. This breakthrough not only enhances the effectiveness of driving assistance systems but also provides reliable scientific support for reducing the risk of accidents caused by fatigued driving. These research findings have significant theoretical and practical implications for advancing intelligent driving assistance technology and improving nighttime road safety.

Nighttime driving presents a critical challenge to road safety due to insufficient lighting and increased risk of driver fatigue. Existing methods for monitoring driver fatigue, mainly focusing on behavioral analysis and biometric monitoring, face significant challenges under low-light conditions. Their effectiveness, especially in dynamic lighting environments, is limited by their dependency on specific environmental conditions and active driver participation, leading to reduced accuracy and practicality in real-world scenarios. This study introduces a novel 'Illumination Intelligent Adaptation and Analysis Framework (IIAAF)', aimed at addressing these limitations and enhancing the accuracy and practicality of driver fatigue monitoring under nighttime low-light conditions. The IIAAF framework employs a multidimensional technology integration, including comprehensive body posture analysis and facial fatigue feature detection, per-pixel dynamic illumination adjustment technology, and a light variation feature learning system based on Convolutional Neural Networks (CNN) and time-series analysis. Through this integrated approach, the framework is capable of accurately capturing subtle fatigue signals in nighttime driving environments and adapting in real-time to rapid changes in lighting conditions. Experimental results on two independent datasets indicate that the IIAAF framework significantly improves the accuracy of fatigue detection under nighttime low-light conditions. This breakthrough not only enhances the effectiveness of driving assistance systems but also provides reliable scientific support for reducing the risk of accidents caused by fatigued driving. These research findings have significant theoretical and practical implications for advancing intelligent driving assistance technology and improving nighttime road safety., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Tian et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The techniques that allow one to estimate measurements at the unsensed points of a system are known as virtual sensing. These techniques are useful for the implementation of condition monitoring systems in industrial equipment subjected to high cyclic loads that can cause fatigue damage, such as industrial presses. In this article, three different virtual sensing algorithms for strain estimation are tested using real measurement data obtained from a scaled bed press prototype: two deterministic algorithms (Direct Strain Observer and Least-Squares Strain Estimation) and one stochastic algorithm (Static Strain Kalman Filter). The prototype is subjected to cyclic loads using a hydraulic fatigue testing machine and is sensorized with strain gauges. Results show that sufficiently accurate strain estimations can be obtained using virtual sensing algorithms and a reduced number of strain gauges as input sensors when the monitored structure is subjected to static and quasi-static loads. Results also show that is possible to estimate the initiation of fatigue cracks at critical points of a structural component using virtual strain sensors.

A wireless monitoring system based on piezoelectric energy harvesting (PEH) is presented to provide fatigue data of wind turbine blades in operation. The system comprises three subsystems, each respectively providing the following functions: (i) the conversion of mechanical to electric energy by exploiting the bistable vibration of a composite beam with piezoelectric patches in post-buckling, (ii) harvesting the converted energy by means of a modified, commercial, off-the-shelf (COTS) circuit to feed a LiPo battery and (iii) the battery-powered acquisition and wireless transmission of sensory signals to the cloud to be elaborated upon by the end-user. The system was verified with ground tests under representative operation conditions, which demonstrated the fulfillment of the design requirements. The measurements indicated that the system provided 23% of the required power for fully autonomous operation when subjected to white noise base excitation of 1 g acceleration in the range of 1–20 Hz.

Abstract The detection of surface electromyography (sEMG) signals on the skin has attracted increasing attention because of its ability to monitor muscle conditions in a noninvasive manner and thus possesses great application potential to assess athletic status and training efficiency in real time or to evaluate postoperative muscle rehabilitation conveniently. Here, a flexible wireless sEMG monitoring system that consists of a stretchable sEMG epidermal patch and a flexible printed circuit board to provide real‐time evaluation of muscle strength and fatigue is reported. The epidermal patch is designed to have good stretchability and permeability and optimized to ensure a low contact impedance with the skin and minimized background noise for sEMG signal acquisition with high fidelity. Six commonly used time‐domain and two frequency‐domain features extracted from sEMG signals are systematically analyzed, and a strategy for feature selection and pattern identification is proposed that eventually enables the real‐time assessment of muscle strength and fatigue by using an integrated system in a wearable form.

The objective measurement of fatigue is of critical relevance in areas such as occupational health and safety as fatigue impairs cognitive and motor performance, thus reducing productivity and increasing the risk of injury. Wearable systems represent highly promising solutions for fatigue monitoring as they enable continuous, long-term monitoring of biomedical signals in unattended settings, with the required comfort and non-intrusiveness. This is a p rerequisite for the development of accurate models for fatigue monitoring in real-time. However, monitoring fatigue through wearable devices imposes unique challenges. To provide an overview of the current state-of-the-art in monitoring variables associated with fatigue via wearables and to detect potential gaps and pitfalls in current knowledge, a systematic review was performed. The Scopus and PubMed databases were searched for articles published in English since 2015, having the terms “fatigue,” “drowsiness,” “vigilance,” or “alertness” in the title, and proposing wearable device-based systems for non-invasive fatigue quantification. Of the 612 retrieved articles, 60 satisfied the inclusion criteria. Included studies were mainly of short duration and conducted in laboratory settings. In general, researchers developed fatigue models based on motion (MOT), electroencephalogram (EEG), photoplethysmogram (PPG), electrocardiogram (ECG), galvanic skin response (GSR), electromyogram (EMG), skin temperature (Tsk), eye movement (EYE), and respiratory (RES) data acquired by wearable devices available in the market. Supervised machine learning models, and more specifically, binary classification models, are predominant among the proposed fatigue quantification approaches. These models were considered to perform very well in detecting fatigue, however, little effort was made to ensure the use of high-quality data during model development. Together, the findings of this review reveal that methodological limitations have hindered the generalizability and real-world applicability of most of the proposed fatigue models. Considerably more work is needed to fully explore the potential of wearables for fatigue quantification as well as to better understand the relationship between fatigue and changes in physiological variables.

About 40% of the US construction workforce experiences high-level fatigue, which leads to poor judgment, increased risk of injuries, a decrease in productivity, and a lower quality of work. Therefore, it is essential to monitor fatigue to reduce its adverse effects and prevent long-term health problems. However, since fatigue demonstrates itself in several complex processes, there is no single standard measurement method for fatigue detection. This study aims to develop a system for continuous workers’ fatigue monitoring by predicting the aerobic fatigue threshold (AFT) using forearm muscle activity and motion data. The proposed system consists of five modules: Data acquisition, activity recognition, oxygen uptake prediction, maximum aerobic capacity (MAC) estimation, and continuous AFT monitoring. The proposed system was evaluated on the participants performing fourteen scaffold-building activities. The results show that the AFT features have achieved a higher accuracy of 92.31% in assessing the workers’ fatigue level compared to heart rate (51.28%) and percentage heart rate reserve (50.43%) features. Moreover, the overall performance of the proposed system on unseen data using average two-min AFT features was 76.74%. The study validates the feasibility of using forearm muscle activity and motion data to workers’ fatigue levels continuously.

The techniques that allow one to estimate measurements at the unsensed points of a system are known as virtual sensing. These techniques are useful for the implementation of condition monitoring systems in industrial equipment subjected to high cyclic loads that can cause fatigue damage, such as industrial presses. In this article, three different virtual sensing algorithms for strain estimation are tested using real measurement data obtained from a scaled bed press prototype: two deterministic algorithms (Direct Strain Observer and Least-Squares Strain Estimation) and one stochastic algorithm (Static Strain Kalman Filter). The prototype is subjected to cyclic loads using a hydraulic fatigue testing machine and is sensorized with strain gauges. Results show that sufficiently accurate strain estimations can be obtained using virtual sensing algorithms and a reduced number of strain gauges as input sensors when the monitored structure is subjected to static and quasi-static loads. Results also show that is possible to estimate the initiation of fatigue cracks at critical points of a structural component using virtual strain sensors. [ABSTRACT FROM AUTHOR]

A wireless monitoring system based on piezoelectric energy harvesting (PEH) is presented to provide fatigue data of wind turbine blades in operation. The system comprises three subsystems, each respectively providing the following functions: (i) the conversion of mechanical to electric energy by exploiting the bistable vibration of a composite beam with piezoelectric patches in post-buckling, (ii) harvesting the converted energy by means of a modified, commercial, off-the-shelf (COTS) circuit to feed a LiPo battery and (iii) the battery-powered acquisition and wireless transmission of sensory signals to the cloud to be elaborated upon by the end-user. The system was verified with ground tests under representative operation conditions, which demonstrated the fulfillment of the design requirements. The measurements indicated that the system provided 23% of the required power for fully autonomous operation when subjected to white noise base excitation of 1 g acceleration in the range of 1-20 Hz.

In this paper, a vision-based physiological signal measurement system is proposed to instantly measure driver fatigue. A remote photoplethysmography (rPPG) signal is a type of physiological signal measured by a camera without any contact device, and it also retains the characteristics of the PPG, which is useful to evaluate fatigue. To solve the inconvenience caused by the traditional contact-based physiological fatigue detection system and to improve the accuracy, the system measures both the motional and physiological information by using one image sensor. In a practical application, the environmental noise would affect the measured signal, and therefore, we performed a preprocessing step on the signal to extract a clear signal. The experiment was designed in collaboration with Taipei Medical University, and a questionnaire-based method was used to define fatigue. The questionnaire that could directly react to the feeling of the subject was treated as our ground truth. The evaluated correlation was 0.89 and the root mean square error was 0.65 for ten-fold cross-validation on the dataset. The trend of driver fatigue could be evaluated without a contact device by the proposed system. This advantage ensures the safety of the driver and reliability of the system.