Collaborative Research: FW-HTF-R: Future of Construction Workplace Health Monitoring

合作研究:FW-HTF-R:建筑工作场所健康监测的未来

基本信息

  • 批准号:
    2401745
  • 负责人:
  • 金额:
    $ 134.45万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2023
  • 资助国家:
    美国
  • 起止时间:
    2023-11-01 至 2026-12-31
  • 项目状态:
    未结题

项目摘要

Given the disproportionate rate of fatalities and injuries in the construction industry and the potential of ambiguous health and hazardous situations with respect to the impending technological revolution and climate change, it is crucial to improve the health and safety of the future workforce. However, there is a lack of an effective, objective, and continuous approach for assessing construction workers' health status at jobsites. Although there have been important innovations in wearable physiological sensing technologies and artificial intelligence for objective assessment of construction workers' health parameters, there remain fundamental challenges for establishing a worker-centered holistic health monitoring approach with promising preventive potentials. These challenges stem from: a) lack of a scalable and feasible wearable sensor for continuous elicitation of workers' diverse bodily responses to stressors in the field; b) lack of a robust interpretive data-driven framework to process the elicited signals for automatic early detection of physical fatigue, mental stress, and exposure to heat stress; and c) lack of effective representation of health and safety information to workers and managers for enabling improved task decisions by augmenting their situational awareness. By establishing a real-time and context-aware holistic health monitoring approach, this project will play a fundamental role in improving the safety of close to 7 million workers in the U.S. construction sector. The developed intelligent health monitoring system is expected to produce changes in the quality of work and workforce policies, resulting in reduced conflicts and enhanced quality of life. It can also be used to address workplace health issues in other hazardous industries such as manufacturing, firefighting, and agriculture.The overarching goal of this research is to improve construction workforce health and safety by integrating multi-disciplinary research in flexible, wearable sensor fabrication, artificial intelligence, and privacy-aware information visualization to provide near-real-time and projected future context-aware health and safety information to workers and managers for enabling improved task decisions by augmenting their situational awareness. The intellectual significance of this project lies in fulfilling the goal by generating and expanding new knowledge on three fronts. First, the project will design and fabricate a flexible wearable sensor for continuous and noninvasive measurement of workers' bioelectric signals and electrochemical responses at construction sites. The use of a single, flexible wearable sensing device instead of multiple off-the-shelf sensors will facilitate the scalability and feasibility of the proposed health sensing system in the construction workplace. Second, the project will develop robust machine learning algorithms and frameworks for continuous and objective assessment of workers' health conditions in the field based on physiological, contextual, and environmental data. For this purpose, this project will address fundamental challenges related to traditional machine learning algorithms by developing a novel interpretive data-driven approach robust to inter- and intra-individual variability while ensuring data security and privacy. Third, this research will generate a digital twin model (health and safety maps) of the construction sites through an array of collective health analyses and develop an automated feedback module for providing personal health-related information and corresponding mitigation strategies to field workers. The insights into the collective health and safety information can profoundly assist the workers and safety managers in making a sound, far-sighted decision about the execution of field-oriented construction operations in near real-time. This research effort will open new doors in improving proactive health and safety management in the field through collective visualization of workers' real-time health and safety information.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.
鉴于建筑行业的死伤率不成比例,以及即将到来的技术革命和气候变化可能导致健康和危险情况不明朗,改善未来劳动力的健康和安全至关重要。然而,缺乏有效、客观和持续的方法来评估工地建筑工人的健康状况。尽管可穿戴生理传感技术和人工智能在客观评估建筑工人健康参数方面取得了重要创新,但建立以工人为中心、具有良好预防潜力的整体健康监测方法仍然存在根本挑战。这些挑战源于:a)缺乏可扩展且可行的可穿戴传感器来持续激发工人对现场压力源的不同身体反应; b) 缺乏强大的解释性数据驱动框架来处理引发的信号,以自动及早检测身体疲劳、精神压力和热应激; c) 缺乏向工人和管理人员有效传达健康和安全信息,无法通过增强他们的态势感知来改进任务决策。通过建立实时和情境感知的整体健康监测方法,该项目将为改善美国建筑行业近 700 万工人的安全发挥基础性作用。开发的智能健康监测系统预计将改变工作质量和劳动力政策,从而减少冲突并提高生活质量。它还可用于解决其他危险行业(例如制造、消防和农业)的工作场所健康问题。这项研究的总体目标是通过整合灵活、可穿戴传感器制造、人工智能和隐私意识信息可视化,为工人和管理人员提供近乎实时和预测的未来情境感知健康和安全信息,通过增强他们的态势感知来改进任务决策。该项目的智力意义在于通过在三个方面产生和扩展新知识来实现​​这一目标。首先,该项目将设计和制造一种灵活的可穿戴传感器,用于连续、无创地测量建筑工地工人的生物电信号和电化学反应。使用单个灵活的可穿戴传感设备而不是多个现成的传感器将促进所提出的健康传感系统在建筑工作场所的可扩展性和可行性。其次,该项目将开发强大的机器学习算法和框架,根据生理、背景和环境数据持续客观地评估现场工人的健康状况。为此,该项目将通过开发一种新颖的解释性数据驱动方法来解决与传统机器学习算法相关的基本挑战,该方法对个体间和个体内的变异性具有鲁棒性,同时确保数据安全和隐私。第三,这项研究将通过一系列集体健康分析生成建筑工地的数字孪生模型(健康和安全地图),并开发一个自动反馈模块,为现场工作人员提供个人健康相关信息和相应的缓解策略。对集体健康和安全信息的洞察可以深刻地帮助工人和安全管理人员近乎实时地就现场施工作业的执行做出合理、有远见的决策。这项研究工作将通过工人实时健康和安全信息的集体可视化,为改善现场主动健康和安全管理打开新的大门。该奖项反映了 NSF 的法定使命,并通过使用基金会的智力评估进行评估,被认为值得支持。优点和更广泛的影响审查标准。

项目成果

期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ monograph.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ sciAawards.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ conferencePapers.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ patent.updateTime }}

Houtan Jebelli其他文献

Unsupervised Adversarial Domain Adaptation in Wearable Physiological Sensing for Construction Workers’ Health Monitoring Using Photoplethysmography
使用光电体积描记法进行建筑工人健康监测的可穿戴生理传感中的无监督对抗域适应
  • DOI:
    10.1061/9780784485262.035
  • 发表时间:
    2024
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Yogesh Gautam;Yizhi Liu;Houtan Jebelli
  • 通讯作者:
    Houtan Jebelli
Enhancing Human-Centric Physiological Data-Driven Heat Stress Assessment in Construction through a Transfer Learning-Based Approach
通过基于迁移学习的方法加强建筑中以人为中心的生理数据驱动的热应激评估
  • DOI:
  • 发表时间:
    2024
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Amit Ojha;Ali Sharifironizi;Yizhi Liu;Houtan Jebelli
  • 通讯作者:
    Houtan Jebelli
Assessing the effects of slippery steel beam coatings to ironworkers' gait stability.
评估光滑钢梁涂层对钢铁工人步态稳定性的影响。
  • DOI:
    10.1016/j.apergo.2017.11.003
  • 发表时间:
    2018
  • 期刊:
  • 影响因子:
    3.2
  • 作者:
    Hyunsook Kim;C. Ahn;T. Stentz;Houtan Jebelli
  • 通讯作者:
    Houtan Jebelli
Pilot Study of Powered Wearable Robot Use for Simulated Flooring Work
动力可穿戴机器人用于模拟地板工作的试点研究
  • DOI:
    10.1061/9780784485224.098
  • 发表时间:
    2024
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Akinwale Okunola;A. Akanmu;Nihar J. Gonsalves;Anthony O. Yusuf;Houtan Jebelli
  • 通讯作者:
    Houtan Jebelli
Human-Robot Co-Adaptation in Construction: Bio-Signal Based Control of Bricklaying Robots
建筑中的人机协同适应:砌砖机器人的生物信号控制

Houtan Jebelli的其他文献

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

{{ truncateString('Houtan Jebelli', 18)}}的其他基金

Investigating the Impact of an Immersive VR-based Learning Environment for Learning Human-Robot Collaboration in Construction Robotics Education
研究基于 VR 的沉浸式学习环境对建筑机器人教育中人机协作学习的影响
  • 批准号:
    2402008
  • 财政年份:
    2023
  • 资助金额:
    $ 134.45万
  • 项目类别:
    Standard Grant
Collaborative Research: NRI: Understanding Underlying Risks and Sociotechnical Challenges of Powered Wearable Exoskeleton to Construction Workers
合作研究:NRI:了解建筑工人动力可穿戴外骨骼的潜在风险和社会技术挑战
  • 批准号:
    2410255
  • 财政年份:
    2023
  • 资助金额:
    $ 134.45万
  • 项目类别:
    Standard Grant
Investigating the Impact of an Immersive VR-based Learning Environment for Learning Human-Robot Collaboration in Construction Robotics Education
研究基于 VR 的沉浸式学习环境对建筑机器人教育中人机协作学习的影响
  • 批准号:
    2235490
  • 财政年份:
    2023
  • 资助金额:
    $ 134.45万
  • 项目类别:
    Standard Grant
Collaborative Research: FW-HTF-R: Future of Construction Workplace Health Monitoring
合作研究:FW-HTF-R:建筑工作场所健康监测的未来
  • 批准号:
    2222654
  • 财政年份:
    2022
  • 资助金额:
    $ 134.45万
  • 项目类别:
    Standard Grant
Collaborative Research: NRI: Understanding Underlying Risks and Sociotechnical Challenges of Powered Wearable Exoskeleton to Construction Workers
合作研究:NRI:了解建筑工人动力可穿戴外骨骼的潜在风险和社会技术挑战
  • 批准号:
    2221167
  • 财政年份:
    2022
  • 资助金额:
    $ 134.45万
  • 项目类别:
    Standard Grant

相似国自然基金

耐蚀性与氢陷阱协同调控的高强度紧固件用钢延迟断裂行为研究
  • 批准号:
  • 批准年份:
    2022
  • 资助金额:
    54 万元
  • 项目类别:
    面上项目
高强紧固件缝隙腐蚀多因素临界条件研究
  • 批准号:
  • 批准年份:
    2022
  • 资助金额:
    54 万元
  • 项目类别:
    面上项目
基于DES/FW-H方法的共轴刚性旋翼气动噪声预测方法及机理研究
  • 批准号:
  • 批准年份:
    2021
  • 资助金额:
    30 万元
  • 项目类别:
    青年科学基金项目
航天紧固件强韧与润滑一体化复合碳基薄膜界面调控及防松机理研究
  • 批准号:
  • 批准年份:
    2020
  • 资助金额:
    59 万元
  • 项目类别:
    面上项目
基于模糊测试的物联网设备固件漏洞检测技术研究
  • 批准号:
  • 批准年份:
    2020
  • 资助金额:
    292 万元
  • 项目类别:
    重点项目

相似海外基金

Collaborative Research [FW-HTF-RL]: Enhancing the Future of Teacher Practice via AI-enabled Formative Feedback for Job-Embedded Learning
协作研究 [FW-HTF-RL]:通过人工智能支持的工作嵌入学习形成性反馈增强教师实践的未来
  • 批准号:
    2326170
  • 财政年份:
    2023
  • 资助金额:
    $ 134.45万
  • 项目类别:
    Standard Grant
Collaborative Research: FW-HTF-RM: Human-in-the-Lead Construction Robotics: Future-Proofing Framing Craft Workers in Industrialized Construction
合作研究:FW-HTF-RM:人类主导的建筑机器人:工业化建筑中面向未来的框架工艺工人
  • 批准号:
    2326160
  • 财政年份:
    2023
  • 资助金额:
    $ 134.45万
  • 项目类别:
    Standard Grant
Collaborative Research: FW-HTF-RL: Trapeze: Responsible AI-assisted Talent Acquisition for HR Specialists
合作研究:FW-HTF-RL:Trapeze:负责任的人工智能辅助人力资源专家人才获取
  • 批准号:
    2326193
  • 财政年份:
    2023
  • 资助金额:
    $ 134.45万
  • 项目类别:
    Standard Grant
Collaborative Research: FW-HTF-RM: Artificial Intelligence Technology for Future Music Performers
合作研究:FW-HTF-RM:未来音乐表演者的人工智能技术
  • 批准号:
    2326198
  • 财政年份:
    2023
  • 资助金额:
    $ 134.45万
  • 项目类别:
    Standard Grant
FW-HTF-RL/Collaborative Research: Future of Digital Facility Management (Future of DFM)
FW-HTF-RL/协作研究:数字设施管理的未来(DFM 的未来)
  • 批准号:
    2326407
  • 财政年份:
    2023
  • 资助金额:
    $ 134.45万
  • 项目类别:
    Standard Grant
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了