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

鉴于建筑业的伤亡率过高，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状况不明确，因此改善未来劳动力的健康和安全至关重要。然而，缺乏一个有效的，客观的，和连续的方法来评估建筑工人的健康状况在工地。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的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。这些挑战来自：a）缺乏可扩展且可行的可穿戴传感器来持续引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，以自动早期检测身体疲劳、精神压力和热应激;以及c）缺乏对工人和管理者的健康和安全信息的有效表示，以使得能够通过增强他们的情境意识来改进任务决策。通过建立实时和环境感知的整体健康监测方法，该项目将在改善美国建筑业近700万工人的安全方面发挥重要作用。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，提高生活质量。它也可以用于解决其他危险行业的工作场所健康问题，如制造业，消防和农业。这项研究的总体目标是通过整合柔性，可穿戴传感器制造，人工智能，和隐私感知信息可视化，以提供近实时和预测的未来环境-向工人和管理人员提供健康和安全信息，以通过增强他们的情境意识来改进任务决策。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，用于连续和非侵入性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感测设备而不是多个现成的传感器将促进所提出的健康感测系统在建筑工作场所中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，以便根据生理，背景和环境数据对现场工人的健康状况进行持续和客观的评估。为此，该项目将通过开发一种新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差异性具有鲁棒性，同时确保数据安全性和隐私性。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，为现场工人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实时地对执行面向现场的施工操作做出合理的、有远见的决策。这项研究工作将打开新的大门，在改善积极主动的健康和安全管理，在现场通过集体可视化的工人的实时健康和安全信息。这个奖项反映了NSF的法定使命，并已被认为是值得的支持，通过评估使用基金会的智力价值和更广泛的影响审查标准。

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.