Perceived benefits, barriers, perceptions, and readiness to use exoskeletons in the construction industry: Differences by demographic characteristics.

Exoskeletons (EXOs) are a promising wearable intervention to reduce work-related musculoskeletal disorder risks among construction workers. However, the adoption of EXOs may differ with demographic characteristics. Survey data (n = 361) were collected from construction industry stakeholders and a summation score method was used to summarize respondent's benefits and barriers to EXO use, along with perceptions and readiness to use. Responses were stratified by race (White vs. non-White), sex (male vs. female), and age (<47 years vs. ≥47 years). Both a higher Benefits score and a higher Perceptions score were significantly and positively associated with a higher Readiness to Use score. There were also significant differences in perceived barriers to EXO use by race and sex. These results demonstrate substantial interest in EXO use but also emphasize the need to ensure proportionate access to the potential benefits of EXO technology.

Unmanned Aerial Systems and Deep Learning for Safety and Health Activity Monitoring on Construction Sites.

Construction is a highly hazardous industry typified by several complex features in dynamic work environments that have the possibility of causing harm or ill health to construction workers. The constant monitoring of workers' unsafe behaviors and work conditions is considered not only a proactive but also an active method of removing safety and health hazards and preventing potential accidents on construction sites. The integration of sensor technologies and artificial intelligence for computer vision can be used to create a robust management strategy and enhance the analysis of safety and health data needed to generate insights and take action to protect workers on construction sites. This study presents the development and validation of a framework that implements the use of unmanned aerial systems (UASs) and deep learning (DL) for the collection and analysis of safety activity metrics for improving construction safety performance. The developed framework was validated using a pilot case study. Digital images of construction safety activities were collected on active construction sites using a UAS, and the performance of two different object detection deep-learning algorithms/models (Faster R-CNN and YOLOv3) for safety hardhat detection were compared. The dataset included 7041 preprocessed and augmented images with a 75/25 training and testing split. From the case study results, Faster R-CNN showed a higher precision of 93.1% than YOLOv3 (89.8%). The findings of this study show the impact and potential benefits of using UASs and DL in computer vision applications for managing safety and health on construction sites.