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.
It is essential to understand the effectiveness of any training program so it can be improved accordingly. Various studies have applied standard metrics for the evaluation of visual behavior to recognize the areas of interest that attract individuals’ attention as there is a high correlation between attentional behavior and where one is focusing on. However, through reviewing the literature, we believe that studies that applied eye-tracking technologies for training purposes are still limited, especially in the industrial energy assessment training field. In this paper, the effectiveness of industrial energy assessment training was quantitatively evaluated by measuring the attentional allocation of trainees using eye-tracking technology. Moreover, this study identifies the areas that require more focus based on evaluating the performance of subjects after receiving the training. Additionally, this research was conducted in a controlled environment to remove the distractions that may be caused by environmental factors to only concentrate on variables that influence the learning behavior of subjects. The experiment results showed that after receiving the training, the subjects’ performance in energy assessment was significantly improved in two areas: production, and recycling and waste management, and the designed training program enhanced the knowledge of participants in identifying energy-saving opportunities to the knowledge level of experienced participants.
Purpose Currently, there is a dearth of research studies regarding macro analysis of the workforce productivity of the US construction industry. The purpose of this paper is to calculate the workforce productivity changes of the US construction industry from 2006 to 2016, with the number of laborers as input and value of construction industry as output. Design/methodology/approach The present study introduced the data envelopment analysis (DEA) based Malmquist productivity index model to measure the workforce productivity of the US construction industry from 2006 to 2016. Findings The results indicated that the workforce productivity of the US construction industry experienced a continuous decline, except for the increases from 2011 to 2013 and from 2014 to 2015. It was also shown that there were gaps in the workforce productivity development level among all states and nine regions in the US construction industry. Besides, the relationship between workforce productivity and four aspects, including real estate price, workforce, climate distribution and economic factors, was analyzed. Research limitations/implications The calculation of the productivity of the US construction industry is based on the premise that the external environment is fixed and unchanged from 2006 to 2016, but the multi-level DEA model for further calculation is required for obtaining more effective conclusions. Social implications This paper measures the workforce productivity of the US construction industry over the past 11 years, which added latest analysis and knowledge into the construction industry, providing decision-makers with advice and data support to formulate policies to improve workforce productivity. Originality/value This study provided both government decision-makers and industrial practitioners with important macro background environment information, which will facilitate the improvement of workforce productivity in the construction industry in different regions of the US.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.