RESUMEN
INTRODUCTION: Several studies have assessed and validated the impact of exoskeletons on back and shoulder muscle activation; however, limited research has explored the role that exoskeletons could play in mitigating lower arm-related disorders. This study assessed the impact of Ironhand, an active hand exoskeleton (H-EXO) designed to reduce grip force exertion, on worker exertion levels using a two-phase experimental design. METHOD: Ten male participants performed a controlled, simulated drilling activity, while three male participants completed an uncontrolled concrete demolition activity. The impact of the exoskeleton was assessed in terms of muscle activity across three different muscles using electromyography (EMG), perceived exertion, and perceived effectiveness. RESULTS: Results indicate that peak muscle activation decreased across the target muscle group when the H-EXO was used, with the greatest reduction (27%) observed in the Extensor Carpi Radialis (ECR). Using the exoskeleton in controlled conditions did not significantly influence perceived exertion levels. Users indicated that the H-EXO was a valuable technology and expressed willingness to use it for future tasks. PRACTICAL APPLICATIONS: This study showcases how glove-based exoskeletons can potentially reduce wrist-related disorders, thereby improving safety and productivity among workers. Future work should assess the impact of the H-EXO in various tasks, different work environments and configurations, and among diverse user groups.
Asunto(s)
Electromiografía , Dispositivo Exoesqueleto , Mano , Humanos , Masculino , Proyectos Piloto , Adulto , Mano/fisiología , Fuerza de la Mano/fisiología , Músculo Esquelético/fisiología , Adulto Joven , Esfuerzo Físico/fisiología , Análisis y Desempeño de Tareas , Industria de la Construcción/instrumentaciónRESUMEN
INTRODUCTION: Active back-support exoskeletons are gaining more awareness as a solution to the prevalence of work-related musculoskeletal disorders in the construction industry. This study aims to understand the factors that influence the adoption of active back-support exoskeletons in the construction industry. METHOD: A literature review was conducted to gather relevant adoption factors related to exoskeleton implementation. Building on the TOE (Technology, Organization, and Environment) framework, two rounds of the survey via the Delphi technique were administered with 13 qualified industry professionals to determine the most important adoption factors using the relative importance index. Through semi-structured interviews, the professionals expressed their perspectives on the impact of active back-support exoskeletons on the construction industry. RESULTS: Important factors included 18 facilitators and 21 barriers. The impact of the exoskeletons in the construction industry was categorized into expected benefits, barriers, solutions, adjustment to technology, implementation, and applicable tasks. CONCLUSIONS: This study identified the factors to be considered in the adoption and implementation of active back-support exoskeletons in the construction industry from the perspective of stakeholders. The study also elucidates the impact of active exoskeletons on construction organizations and the broader environment. PRACTICAL APPLICATIONS: This study provides useful guidance to construction companies interested in adopting active back-support exoskeletons. Our results will also help manufacturers of active back-support exoskeletons to understand the functional requirements and adjustments required for utilization in the construction industry. Lastly, the study expands the application of the TOE framework to the adoption of active back-support exoskeletons in the construction industry.
Asunto(s)
Industria de la Construcción , Técnica Delphi , Dispositivo Exoesqueleto , Enfermedades Musculoesqueléticas , Humanos , Enfermedades Musculoesqueléticas/prevención & control , Enfermedades Profesionales/prevención & control , Encuestas y CuestionariosRESUMEN
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.
Asunto(s)
Industria de la Construcción , Dispositivo Exoesqueleto , Enfermedades Musculoesqueléticas , Humanos , Masculino , Femenino , Persona de Mediana Edad , Encuestas y Cuestionarios , Enfermedades Musculoesqueléticas/prevención & control , DemografíaRESUMEN
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.