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HUMAN-CENTRIC NON-INVASIVE PHYSIOLOGICAL SENSING SYSTEM FOR EARLY DETECTION OF WORKERS? HEAT STRESS IN THE FIELD

HUMAN-CENTRIC NON-INVASIVE PHYSIOLOGICAL SENSING SYSTEM FOR EARLY DETECTION OF WORKERS? HEAT STRESS IN THE FIELD
以人为本的非侵入式生理传感系统,用于早期检测工人?
批准号:
10527981
负责人:
Houtan Jebelli
金额:
$22.96万
依托单位国家:
美国
项目类别:
财政年份:
2022
资助国家:
美国
项目状态:
已结题
起止时间:
2022-09-01 至 2024-08-31

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中文摘要
翻译
以人为中心的非侵入性生理传感系统,用于早期检测 野外作业工人的热应激 项目摘要 热应激会突然升高核心体温超过安全阈值,关闭温度- 调节系统,并导致严重的器官衰竭,甚至死亡。热应激的长期后果 会增加患心血管、呼吸道疾病和慢性肾脏疾病的机会。由于 为了进行密集的体力劳动和使用防护服,许多工人在热脆弱的行业,如 因为建筑、消防和农场工作,以及士兵和运动员等其他人群, 有限的一套基本预防措施,以防止创伤或灾难性的热应激。此外,目前 热应力法规是基于过于普遍的环境或工作条件,而不是特定于 个体这些方法没有考虑到工人的身体和生理特征, 反映了即使在相似的条件下,工人对热暴露的不同反应, 正在经历不同的情况。因此,早期热应激预测的任何详细方案几乎都是 完全缺失,热应激干预通常在暴露变得严重之后很久才发生。 这个R21项目的首要目标是开发和评估一个以工作人员为中心的热应激 基于生理和环境信号的监测框架。该系统预测工人的 通过持续解读生物信号和环境信号, 通过实时使用上下文敏感、数据驱动、机器学习模型。这个项目 旨在防止死亡和灾难性的伤害,如在劳动密集型行业的脑损伤, 改进热创伤治疗,但缺乏敏感手段。因此,拟议的框架是一个关键步骤, 实现NIOSH战略目标6:“改善工作场所安全,减少创伤性伤害”,为诺拉 部门21,采矿,中期目标6.9,“过度热暴露”和战略目标7,“促进安全 诺拉第23部门(建筑)的中期目标7.1,“非 标准工作安排。“拟议的研究将遵循我们的具体目标, 目标。第一个目标是设计和制作一个非侵入性的无线生理传感系统, 诱发生理反应(光电体积描记法[PPG],皮肤电活动[EDA], 心电图[ECG]、皮肤温度[ST]和核心温度[Tc]),可以主要评估 工人的热应激暴露。第二个目标是开发一个基于生理学的数据驱动框架, 工人热应激暴露的早期预测。第三个目标是评估开发的性能 无线传感系统和预测数据驱动的框架在受控和自然的环境。 建议的系统具有很高的潜力,以防止工人从严重的热相关的伤害,触发 及时向工人反馈安全情况。拟议中的系统还将允许新工人安全地开始工作, 培养对炎热条件的耐受力,并适应工作场所。
英文摘要
HUMAN-CENTRIC NON-INVASIVE PHYSIOLOGICAL SENSING SYSTEM FOR EARLY DETECTION OF WORKERS' HEAT STRESS IN THE FIELD PROJECT SUMMARY Heat stress can abruptly raise the core body temperature above a safe threshold, shut down the temperature- regulating system, and result in severe organ failure and even death. Long-term consequences of heat stress can increase the chances of developing cardiovascular, respiratory diseases, and chronic kidney diseases. Due to intensive physical labor and the use of protective clothing, many workers in heat-vulnerable industries, such as construction, firefighting, and farm work, among other populations such as soldiers and athletes, have a limited set of fundamental precautions for preventing traumatic or catastrophic heat stress. Also, the current heat-stress regulations are based on overly general environmental or working conditions, not specific to individuals. These methods do not account for workers' physical and physiological characteristics, and they may reflect dissimilar responses to heat exposure even under similar conditions or similar responses when workers are experiencing different conditions. As a result, any detailed protocol for early heat stress prediction is almost entirely missing, and heat-stress interventions generally occur well after exposure becomes critical. The overarching goal of this R21 project is to develop and evaluate a worker-centered heat stress monitoring framework based on physiological and environmental signals. This system predicts workers' heat-stress exposure at job sites by continuously interpreting biosignals and environmental signals through the use of context-sensitive, data-driven, machine-learning models in real-time. This project intends to prevent deaths and catastrophic injuries such as brain damage in labor-intensive industries that need to improve heat-trauma procedures but lack sensitive means. As such, the proposed framework is a critical step towards reaching key NIOSH strategic goal 6: "improve workplace safety to reduce traumatic injuries" for NORA sector 21, mining, with intermediate goal 6.9, "excessive heat exposure," and strategic goal 7: "promote safe and healthy work design and well-being" for NORA sector 23, construction, with intermediate goal 7.1, "non- standard work arrangements." The proposed research will pursue NIOSH's strategic goals following our specific aims. The first aim is to design and fabricate a non-invasive wireless physiological sensing system for continuous elicitation of physiological responses (photoplethysmography [PPG], electrodermal activity [EDA], electrocardiogram [ECG], skin temperature [ST], and core temperature [Tc]) that can predominantly assess workers' heat-stress exposure. The second aim is to develop a physiologically based data-driven framework for early prediction of workers' heat stress exposure. The third aim is to evaluate the performance of the developed wireless sensing system and predictive data-driven framework in both a controlled and naturalistic environment. The proposed system has a high potential to prevent workers from severe heat-related injuries by triggering safety feedbacks to workers timely. The proposed system will also allow new workers to safely start their job, build up a tolerance for hot conditions, and acclimatize to the workplace.
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