Automated worker action recognition helps to understand the state of workers’ actions, enabling effective management of work performance in terms of productivity, safety, and health issues. A wristband equipped with an accelerometer (e.g., activity tracker) allows to collect the data related to workers’ hand activities without interfering with their ongoing work. Considering that many construction activities involve unique hand movements, the use of acceleration data from a wristband has great potential for action recognition of construction activities. In this context, the authors examine the feasibility of the wrist-worn accelerometer-embedded activity tracker for automated action recognition. Specifically, masonry work was conducted to collect acceleration data in a laboratory. The classification accuracy of four classifiers—the k-nearest neighbor, multilayer perceptron, decision tree, and multiclass support vector machine—was analyzed with different window sizes to investigate classification performance. It was found that the multiclass support vector machine with a 4-s window size showed the best accuracy (88.1%) to classify four different subtasks of masonry work. The present study makes noteworthy contributions to the current body of knowledge. First, the study allows for automatic construction action recognition using a single wrist-worn sensor without interfering with workers’ ongoing work, which can be widely deployed to construction sites. The use of a single sensor also greatly reduces the burden to carry multiple sensors while also reducing computational cost and memory. Second, influences associated with the variability of movement between subject and experience group were examined; thus, a consideration of data acquisition that reflects the characteristics of workers’ actions is suggested.

Inertial Motion Capture (IMC) systems enable in situ studies of human motion free of the severe constraints imposed by Optical Motion Capture systems. Inverse dynamics can use those motions to estimate forces and moments developing within muscles and joints. We developed an inverse dynamic whole-body model that eliminates the usage of force plates (FPs) and uses motion patterns captured by an IMC system to predict the net forces and moments in 14 major joints. We validated the model by comparing its estimates of Ground Reaction Forces (GRFs) to the ground truth obtained from FPs and comparing predictions of the static model’s net joint moments to those predicted by 3D Static Strength Prediction Program (3DSSPP). The relative root-mean-square error (rRMSE) in the predicted GRF was 6% and the intraclass correlation of the peak values was 0.95, where both values were averaged over the subject population. The rRMSE of the differences between our model’s and 3DSSPP predictions of net L5/S1 and right and left shoulder joints moments were 9.5%, 3.3%, and 5.2%, respectively. We also compared the static and dynamic versions of the model and found that failing to account for body motions can underestimate net joint moments by 90% to 560% of the static estimates.

Physically demanding and repetitive tasks expose workers to work-related musculoskeletal disorders (WMSDs). Over the last few decades, various rule-based postural assessment systems have been developed and widely used to facilitate the measurement and evaluation of risks related to WMSDs in many industries. However, the applicability of rule-based assessment to tasks involving heavy material handling has not yet been examined. This study investigated the applicability of three rule-based assessment systems (RULA, REBA, and OWAS) to a bricklaying task. To achieve this goal, an automated assessment tool was developed to implement those systems on whole-body data sets consisting of static postures captured by wearable inertial measurement unit suits. The study demonstrates the use of this tool in assessing risk levels (grand scores) encountered by 43 masons during the laying of 16.6-kg concrete masonry units (CMUs) in a standard wall. Furthermore, the biomechanical analysis of the same data set was carried out and utilized as ground truth to evaluate those results. It was found that rule-based assessment may lead to erroneously inflated risk evaluation in heavy manual handling tasks. In contrast, biomechanical analysis provided sensitive risk evaluations that distinguish the different degrees of risk arising from different motion patterns while participants performed the same tasks. These findings suggest using biomechanical analysis as an objective and robust method to evaluate risks encountered in tasks involving heavy material handling.

Masons regularly perform physically strenuous and demanding duties that may exceed a safe limit. Such activities can contribute to an early retirement for masons, resulting in a shortage of skilled craft workers. Previous ergonomic studies have observed that workers develop safer and more productive work techniques as they gain experience. This study aims to analyze relationships between body loads, experience, and work methods. Specifically, we expanded a previous pilot study by increasing the number of participants from 21 masons to 66 masons. Participants completed a prebuilt standard concrete masonry unit (CMU) lead wall using 45 CMUs. Motion capture suits were used to capture masons’ motions, and a combined biomechanical-productivity analysis was carried out to determine the loads experienced by major body joints. Exploiting the larger dataset, this study assessed how different experience groups load their joints and adjust their work techniques as the work height changes. The results suggested that experienced journeymen adopt similar work techniques distinct from those of less experienced workers. Further, training apprentices to adopt these work methods can help reduce occupational injuries and improve productivity. The results show that the journeymen with more than 20 years of experience adopt safer and more productive work techniques distinct from those of less experienced workers. The present study contributes to the body of knowledge on masons’ safety and productivity by providing an in-depth understanding of the linkage between body loads, work experience, techniques, and productivity. Additionally, the findings in this study are expected to have a greater impact when they are adopted to apprentice-training methods and applied to other high musculoskeletal-disorders-risk trades.

In the era of Industry 4.0, marked by the integration of digitization, automation, and data synthesis, emerging technologies play a vital role in mitigating ergonomic hazards within construction work environments. This study investigates the research trends encompassing the adoption of three categories of emerging technologies—(1) wearable sensors; (2) extended reality, which combines virtual reality (VR), augmented reality (AR), and mixed reality (MR); and (3) exoskeletons and robotics—as the means to mitigate the risk of occupational nonfatal injuries in the construction industry. Employing bibliometric and scientometric analyses, a quantitative examination of the relationship in the literature is performed. From the Scopus database, 347 papers were selected from a pool of 1603 publications from 2018 to 2022. The conducted scientometric analyses encompass annual publication trends, keyword co-occurrence analysis, journal-source analysis, author analysis, and country analysis using VOSviewer (version 1.6.19) and bibliometrix software (version 4.1.3). The findings highlight the crucial role of advanced technologies in enhancing safety and health management in the construction industry. Wearable sensors, for example, offer promising capabilities for real-time monitoring, potentially reducing the risk of onsite injuries by alerting workers to hazards. Extended reality, especially VR, can enhance the effectiveness of safety-training education by simulating realistic scenarios while minimizing exposures to hazardous conditions that workers may face onsite challenges. Furthermore, the integration of exoskeletons and robotics has the potential to reduce physical strain and injury risks among workers, particularly in physically demanding tasks. The review paper identifies current research trends in applying emerging technologies to occupational safety and health within the construction industry, while also suggesting future research directions in this dynamic field.