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: 2222880
• 负责人: Jingang Yi
• 金额: $ 108万
• 依托单位: Rutgers University New Brunswick
• 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=2222880&HistoricalAwards=false
• 关键词: 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付出巨大的体力劳动，并面临严重的安全和健康风险。建筑业是美国风险最高的行业之一。建筑业熟练工人的严重短缺，加剧了改善工人安全和健康的必要性。此外，目前的劳动力正在老化和退休; 2020年约39%的建筑工人年龄在45至64岁之间。年轻人的低兴趣和女性工人的低代表性（2020年仅为4%）正在加剧现有的劳动力短缺。因此，迫切需要开发新技术，以确保工人的安全和无伤害，使该行业更具包容性和经济可持续性，并最终改变建筑工作不安全，技术含量低和男性主导的负面形象。FW-HTF研究项目的目标是为增加的劳动力开发可穿戴安全传感和辅助机器人-工人协作，从而提高工人的保留率，吸引妇女和年轻工人从事建筑职业。研究人员还将开发一些综合研究和教育项目，以吸引来自代表性不足群体的学生进入工程领域，并让本科生参与研究。尽管机器人技术的使用越来越多，但大多数研究都集中在它们如何支持建筑任务并产生经济效益。很少有研究讨论如何部署可穿戴外骨骼来预防与工作相关的肌肉骨骼疾病，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效益、风险和障碍，并促进建设包容、多样化和可持续的劳动力队伍。可穿戴设备、机器学习以及虚拟现实、增强现实和混合现实技术为彻底改变现有的建筑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人工人、组织和建筑行业，以加强工人的安全和健康，提高生产力，解决与性别和年龄相关的劳动力短缺问题，并扩大就业机会。在这个研究项目中，研究人员团队计划为熟练的建筑工人开发可穿戴职业安全传感和辅助机器人协作技术。具体而言，该项目将强调：（1）支持机器学习的实时工人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）可穿戴安全传感和辅助机器人协作，以增强劳动力;（4）分析拟议技术的社会经济影响;及（5）试验研究、工业部署及劳动力培训。学术合作和多方利益相关者的伙伴关系将提供必要的知识和人员基础设施，以解决多学科，多方面的挑战，通过整合最佳实践与新兴技术的建设。这个奖项反映了NSF的法定使命，并已被认为是值得通过评估使用基金会的知识价值和更广泛的影响审查标准的支持。

项目成果

Wearable Sensing and Knee Exoskeleton Control for Awkward Gaits Assistance

• DOI: 10.1109/case49997.2022.9926655
• 发表时间: 2022-08
• 期刊: 2022 IEEE 18th International Conference on Automation Science and Engineering (CASE)
• 作者: Chunchu Zhu;Feng Han;J. Yi

Neural Balance Control of Human Quiet Stance in Construction Tasks

施工任务中人体安静姿态的神经平衡控制

• DOI: 10.1109/case56687.2023.10260384
• 发表时间: 2023
• 期刊: IEEE
• 作者: Sreenivasan, Gayatri;Zhu, Chunchu;Yi, Jingang
• 通讯作者: Yi, Jingang