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.

Metal-working fluids (MWFs) are used in machining and grinding operations to cool the tool and work, reduce the friction between the tool and work, improve the surface integrity of the work piece, and increase tool life and productivity. Health problems have been reported among workers exposed to MWFs, including incidences of respiratory, digestive and skin cancers, and increased rates of cough and phlegm. This paper reviews and discusses issues concerning health risks from exposure to MWFs in machining and grinding operations, the various factors that influence the degree of exposure, and control methods to reduce exposure to metal-working fluids.

The workforce shortage is one of the significant problems in the construction industry. To overcome the challenges due to workforce shortage, various researchers have proposed wearable sensor-based systems in the area of construction safety and health. Although sensors provide rich and detailed information, not all sensors can be used for construction applications. This study evaluates the data quality and reliability of forearm electromyography (EMG) and inertial measurement unit (IMU) of armband sensors for construction activity classification. To achieve the proposed objective, the forearm EMG and IMU data collected from eight participants while performing construction activities such as screwing, wrenching, lifting, and carrying on two different days were used to analyze the data quality and reliability for activity recognition through seven different experiments. The results of these experiments show that the armband sensor data quality is comparable to the conventional EMG and IMU sensors with excellent relative and absolute reliability between trials for all the five activities. The activity classification results were highly reliable, with minimal change in classification accuracies for both the days. Moreover, the results conclude that the combined EMG and IMU models classify activities with higher accuracies compared to individual sensor models.

The purpose of this study was to evaluate the effect of progressive fatigue on factors that previously have been associated with increased risk of low back pain in various occupational settings, during a repetitive lifting task where a freestyle lifting technique was used. A laboratory experiment was conducted to evaluate electromyography amplitude, kinematic, and kinetic parameters of repetitive freestyle lifting during a 2-hour lifting period. Subjective fatigue rating increased over time, indicating that the participant “felt” increasingly fatigued as the experiment progressed. Static composite strength decreased an average of 20% from the beginning to the end of the experiment. Effect of lifting posture (semi-squat, semi-stoop, and stoop) was observed on peak trunk flexion angle, trunk flexion angle at initiation of the lift, and knee angle at initiation of the lift indicating that, in freestyle lifting, participants assume quantitatively different lifting techniques. A significant effect of the time–posture interaction was observed on the dynamic leg lift floor to knuckle height strength, indicating that dynamic strength may change over time depending on lifting posture selected.

BACKGROUND: There is an extensive body of research reviewing the ergonomics needs of industrial office workers. However, very few studies have considered evaluating the working postures of students or professors in universities who are exposed to pro