Non-invasive detection of mental fatigue in construction equipment operators through geometric measurements of facial features.

INTRODUCTION: Prolonged operation of construction equipment could lead to mental fatigue, which can increase the chances of human error-related accidents as well as operators' ill-health. The objective detection of operators' mental fatigue is crucial for reducing accident risk and ensuring operator health. Electroencephalography, photoplethysmography, electrodermal activity, and eye-tracking technology have been used to mitigate this issue. These technologies are invasive and wearable sensors that can cause irritation and discomfort. Geometric measurements of facial features can serve as a noninvasive alternative approach. Its application in detecting mental fatigue of construction equipment operators has not been reported in the literature. Although the application of facial features has been widespread in other domains, such as drivers and other occupation scenarios, their ecological validity for construction excavator operators remains a knowledge gap. METHOD: This study proposed employing geometric measurements of facial features to detect mental fatigue in construction equipment operators' facial features. In this study, seventeen operators performed excavation operations. Mental fatigue was labeled subjectively and objectively using NASA-TLX scores and EDA values. Based on geometric measurements, facial features (eyebrow, mouth outer, mouth corners, head motion, eye area, and face area) were extracted. RESULTS: The results showed that there was significant difference in the measured metrics for high fatigue compared to low fatigue. Specifically, the most noteworthy variation was for the eye and face area metrics, with mean differences of 45.88% and 26.9%, respectively. CONCLUSIONS: The findings showed that geometrical measurements of facial features are a useful, noninvasive approach for detecting the mental fatigue of construction equipment operators.

Evaluation of sweat-based biomarkers using wearable biosensors for monitoring stress and fatigue: a systematic review.

Objectives. This systematic review aims to report the evaluation of wearable biosensors for the real-time measurement of stress and fatigue using sweat biomarkers. Methods. A thorough search of the literature was carried out in databases such as PubMed, Web of Science and IEEE. A three-step approach for selecting research articles was developed and implemented. Results. Based on a systematic search, a total of 17 articles were included in this review. Lactate, cortisol, glucose and electrolytes were identified as sweat biomarkers. Sweat-based biomarkers are frequently monitored in real time using potentiometric and amperometric biosensors. Wearable biosensors such as an epidermal patch or a sweatband have been widely validated in scientific literature. Conclusions. Sweat is an important biofluid for monitoring general health, including stress and fatigue. It is becoming increasingly common to use biosensors that can measure a wide range of sweat biomarkers to detect fatigue during high-intensity work. Even though wearable biosensors have been validated for monitoring various sweat biomarkers, such biomarkers can only be used to assess stress and fatigue indirectly. In general, this study may serve as a driving force for academics and practitioners to broaden the use of wearable biosensors for the real-time assessment of stress and fatigue.

Performance Evaluation of Plastic Concrete Modified with E-Waste Plastic as a Partial Replacement of Coarse Aggregate.

Plastic electronic waste (E-waste) is constantly growing around the world owing to the rapid increase in industrialization, urbanization, and population. The current annual production rate of E-waste is 3-4% in the world and is expected to increase to 55 million tons per year by 2025. To reduce the detrimental impact on the environment and save natural resources, one of the best solutions is to incorporate waste plastic in the construction industry to produce green concrete. This study examines the use of manufactured plastic coarse aggregate (PCA) obtained from E-waste as a partial replacement of natural coarse aggregate (NCA) in concrete. Six types of concrete mix with 10%, 20%, 30%, 40%, and 50% substitution of NCA (by volume) with PCA are prepared and tested. This study investigates the effect of manufactured PCA on the fresh and hardened characteristics of concrete. The properties of recycled plastic aggregate concrete (RPAC) studied include workability, fresh density, dry density, compressive strength (CS), splitting tensile strength (STS), flexural strength (FS), sorptivity coefficient, abrasion resistance, ultrasonic pulse velocity (UPV), and alternate wetting and drying (W-D). The results indicate that the CS, STS, and FS of RPAC declined in the range of 9.9-52.7%, 7.8-47.5%, and 11-39.4%, respectively, for substitution ratios of 10-50%. However, the results also indicate that the incorporation of PCA (10-50%) improved the workability and durability characteristics of concrete. A significant decrement in the sorptivity coefficient, abrasion loss, and UPV value was observed with an increasing amount of PCA. Furthermore, RPAC containing different percentages of PCA revealed better results against alternate W-D cycles with respect to ordinary concrete.

Exploring the Injury Severity Risk Factors in Fatal Crashes with Neural Network.

A better understanding of circumstances contributing to the severity outcome of traffic crashes is an important goal of road safety studies. An in-depth crash injury severity analysis is vital for the proactive implementation of appropriate mitigation strategies. This study proposes an improved feed-forward neural network (FFNN) model for predicting injury severity associated with individual crashes using three years (2017-2019) of crash data collected along 15 rural highways in the Kingdom of Saudi Arabia (KSA). A total of 12,566 crashes were recorded during the study period with a binary injury severity outcome (fatal or non-fatal injury) for the variable to be predicted. FFNN architecture with back-propagation (BP) as a training algorithm, logistic as activation function, and six number of hidden neurons in the hidden layer yielded the best model performance. Results of model prediction for the test data were analyzed using different evaluation metrics such as overall accuracy, sensitivity, and specificity. Prediction results showed the adequacy and robust performance of the proposed method. A detailed sensitivity analysis of the optimized NN was also performed to show the impact and relative influence of different predictor variables on resulting crash injury severity. The sensitivity analysis results indicated that factors such as traffic volume, average travel speeds, weather conditions, on-site damage conditions, road and vehicle type, and involvement of pedestrians are the most sensitive variables. The methods applied in this study could be used in big data analysis of crash data, which can serve as a rapid-useful tool for policymakers to improve highway safety.

Cardiorespiratory and Thermoregulatory Parameters Are Good Surrogates for Measuring Physical Fatigue during a Simulated Construction Task.

Cardiorespiratory (e.g., heart rate and breathing rate) and thermoregulatory (e.g., local skin temperature and electrodermal activity) responses are controlled by the sympathetic nervous system. To cope with increased physical workload, the sympathetic system upregulates its activity to generate greater sympathetic responses (i.e., increased heart rate and respiratory rate). Therefore, physiological measures may have the potential to evaluate changes in physical condition (including fatigue) during functional tasks. This study aimed to quantify physical fatigue using wearable cardiorespiratory and thermoregulatory sensors during a simulated construction task. Twenty-five healthy individuals (mean age, 31.8 ± 1.8 years) were recruited. Participants were instructed to perform 30 min of a simulated manual material handling task in a laboratory. The experimental setup comprised a station A, a 10-metre walking platform, and a station B. Each participant was asked to pick up a 15 kg ergonomically-designed wooden box from station A and then carried it along the platform and dropped it at station B. The task was repeated from B to A and then A to B until the participants perceived a fatigue level > 15 out of 20 on the Borg-20 scale. Heart rate, breathing rate, local skin temperature, and electrodermal activity at the wrist were measured by wearable sensors and the perceived physical fatigue was assessed using the Borg-20 scale at baseline, 15 min, and 30 min from the baseline. There were significant increases in the heart rate (mean changes: 50 ± 13.3 beats/min), breathing rate (mean changes: 9.8 ± 4.1 breaths), local skin temperature (mean changes: 3.4 ± 1.9 °C), electrodermal activity at the right wrist (mean changes: 7.1 ± 3.8 µS/cm), and subjective physical fatigue (mean changes: 8.8 ± 0.6 levels) at the end of the simulated construction task (p < 0.05). Heart rate and breathing rate at 15 and 30 min were significantly correlated with the corresponding subjective Borg scores (p < 0.01). Local skin temperature at 30 min was significantly correlated with the corresponding Borg scores (p < 0.05). However, electrodermal activity at the right wrist was not associated with Borg scores at any time points. The results implied cardiorespiratory parameters and local skin temperature were good surrogates for measuring physical fatigue. Conversely, electrodermal activity at the right wrist was unrelated to physical fatigue. Future field studies should investigate the sensitivity of various cardiorespiratory and thermoregulatory parameters for real time physical fatigue monitoring in construction sites.

Use of Ultra Wide Band Real-Time Location System on Construction Jobsites: Feasibility Study and Deployment Alternatives.

Ultra wide band (UWB)-based real-time location systems (RTLSs) have been widely adopted in the manufacturing industry for tracking tools, materials, and ensuring safety. Researchers in the construction domain have investigated similar uses for UWB-based RTLSs on construction jobsites. However, most of these investigations comprised small-scale experiments using average accuracy only to demonstrate use cases for the technology. Furthermore, they did not consider alternative deployment scenarios for practically feasible deployment of the technology. To overcome these limitations, a series of experiments were performed to study the feasibility of a commercially available RTLS on the construction jobsites. The focus of the work was on feasibility in terms of accuracy analysis of the system for a large experimental site, the level of effort requirements for deployment, and the impact of deployment alternatives on the accuracy of the system. The results found that average accuracy was found to be a misleading indicator of the perceived system performance (i.e., 95th percentile values were considerably higher than average values). Moreover, accuracy is significantly affected by the deployment alternatives. Collectively, the results arising from the study could help construction/safety managers in decision making related to the deployment of UWB-based RTLSs for their construction sites.

The prevalence of musculoskeletal symptoms in the construction industry: a systematic review and meta-analysis.

PURPOSE: Although individual studies have reported high prevalence of musculoskeletal symptoms (MSS) among construction workers, no systematic review has summarized their prevalence rates. Accordingly, this systematic review/meta-analysis aimed to synthesize MSS prevalence in different construction trades, gender and age groups, which may help develop specific ergonomic interventions. METHODS: Nine databases were searched for articles related to the research objective. Two reviewers independently screened citations, extracted information and conducted quality assessment of the included studies. Meta-analyses were conducted on clinical and statistical homogenous data. RESULTS: Thirty-five out of 1130 potential citations were included reporting diverse types of period prevalence and case definitions. Only the 1-year prevalence rates of MSS (defined as at least one episode of pain/MSS in the last year) at nine anatomical regions had sufficient homogeneous data for meta-analysis. Specifically, the 1-year prevalence of MSS was 51.1% for lower back, 37.2% for knee, 32.4% for shoulder, 30.4% for wrist, 24.4% for neck, 24.0% for ankle/foot, 20.3% for elbow, 19.8% for upper back, and 15.1% for hip/thigh. Female workers demonstrated a higher prevalence of MSS while there was insufficient information on the prevalence of trade-specific or age-related MSS. The quality assessments revealed that many included studies estimated prevalence solely based on self-reported data, and did not report non-respondents' characteristics. CONCLUSIONS: Lumbar, knee, shoulder, and wrist MSS are the most common symptoms among construction workers. Future studies should standardize the reporting of period prevalence of MSS in different construction trades to allow meta-analyses and to develop relevant MSS prevention program.