Collaborative Research: FW-HTF-R: Wearable Safety Sensing and Assistive Robot-Worker Collaboration for an Augmented Workforce in Construction

合作研究：FW-HTF-R：可穿戴安全传感和辅助机器人工人协作，增强建筑劳动力

• 批准号: 2222881
• 负责人: Chao Wang
• 金额: $ 72万
• 依托单位: Louisiana State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2222881&HistoricalAwards=false
• 关键词: Collaborative; Research; FW; HTF; Wearable; Collaborative; Research; FW; HTF; Wearable

Construction workers exert intense physical effort and experience serious safety and health risks in hazardous working environments. Thus, the construction industry is one of the highest-risk sectors in the US. A significant shortage of skilled workers in the construction industry amplifies the need to improve workers’ safety and health. Furthermore, the current workforce is aging and retiring; approximately 39% of construction workers were between 45-64 years old in 2020. Low interest among young adults and very low representation of women workers (only 4% in 2020) is exacerbating the existing labor shortage. As a result, there is an urgent need to develop new technology that keeps workers safe and injury-free, makes the industry more inclusive and economically sustainable, and eventually changes negative images that construction jobs are unsafe, low tech, and too male-dominated. The objective of this FW-HTF research project is to develop wearable safety sensing and assistive robot-worker collaboration for an augmented workforce, thereby improving worker retention and attracting women and young workers to construction careers. The researchers will also develop a number of integrated research and education programs to attract students from underrepresented groups into engineering and involve undergraduate students in research.Although robotics technologies are increasingly used, most research focuses on how they support construction tasks and yield economic benefits. Few studies discuss how to deploy wearable exoskeletons to prevent work-related musculoskeletal disorders and improve workers’ safety and health. New interventions are needed to address current safety and health knowledge gaps, identify social and economic benefits, risks, and barriers to the adoption of emerging technologies, and contribute to the development of an inclusive, diverse, and sustainable workforce in construction. Wearable devices, machine learning, and virtual-, augmented- and mixed-reality technologies offer great promise for revolutionizing existing practices in construction. This potential motivates the PIs to develop an integrated, multidisciplinary approach to bring these emerging technologies to individual workers, organizations, and the construction industry to enhance worker safety and health, improve productivity, address gender- and age-related labor shortages and expand employment opportunities. In this research project, the team of researchers plans to develop wearable occupational safety sensing and assistive robotic collaboration technology for skilled construction workers. Specifically, this project will emphasize: (1) machine learning-enabled, real-time worker activity recognition and pose estimation; (2) user-centered design of soft exoskeletons; (3) mixed reality-enhanced work skill transferring and workplace-based learning; (3) wearable safety sensing and assistive robotic collaboration for an augmented workforce; (4) analyses of social-economic impacts of the proposed technology; and (5) pilot studies, industrial deployment and workforce training. Academic collaborations and multi-stakeholder partnerships will provide the intellectual and personnel infrastructure necessary to address the multi-disciplinary, multi-faceted challenges by integrating best practices in construction with emerging technologies.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

建筑工人在危险的工作环境中施加巨大的身体努力，并经历严重的安全和健康风险。那是建筑业是美国最高风险的行业之一。建筑行业放大器中熟练的工人的大量短缺需要提高工人的安全和健康。此外，目前的劳动力正在老化和退休；大约39％的建筑工人在2020年的45-64岁之间。年轻人的兴趣低，女工的代表性很低（2020年仅4％）加剧了现有的劳动力短缺。结果，迫切需要开发新技术，以确保工人安全且无伤害，使行业更具包容性和经济可持续性，有时会改变建筑业不安全，低技术且过于男性主导的负面图像。该FW-HTF研究项目的目的是开发可穿戴的安全敏感性和辅助机器人工人合作，以增强劳动力，从而改善工人的保留率，并吸引妇女和年轻工人从事建筑业的职业。研究人员还将开发许多综合的研究和教育计划，以吸引人数不足的群体的学生进入工程学，并参与研究生。尽管越来越多地使用了机器人技术技术，但大多数研究都集中于他们如何支持建筑任务并产生经济利益。很少有研究讨论如何部署可穿戴外骨骼以防止与工作有关的肌肉骨骼疾病并改善工人的安全和健康。需要采取新的干预措施来解决当前的安全和健康知识差距，确定采用新兴技术的社会和经济利益，风险以及障碍，并为建设中包容，潜水员和可持续的劳动力的发展做出贡献。可穿戴设备，机器学习和虚拟，增强和混合现实的技术为革新现有建筑中的实践提供了巨大的希望。这种潜力促使PI开发一种综合的多学科方法，将这些新兴技术带给个别工人，组织和建筑行业，以提高工人的安全和健康，提高生产力，解决与性别相关的劳动力短缺并扩大就业机会。在该研究项目中，研究人员计划为熟练的建筑工人开发可穿戴的职业安全敏感性和辅助机器人协作技术。具体而言，该项目将强调：（1）基于机器学习的实时工作活动识别和姿势估计； （2）以用户为中心的软外骨骼设计； （3）混合现实增强的工作技能转移和基于工作场所的学习； （3）可穿戴安全感和辅助机器人协作，以增强劳动力； （4）对拟议技术的社会经济影响分析； （5）试点研究，工业部署和劳动力培训。 Academic collaborations and multi-stakeholder partnerships will provide the intellectual and personal infrastructure necessary to address the multi-disciplinary, multi-faceted challenges by integrating best practices in construction with emerging technologies.This award reflects NSF's Statutory mission and has been deemed precious of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.