FW-HTF-P: Understanding and Assessing Technology Innovation to Mitigate Distraction and Improve the Performance of Future Forklift Operators

这项由人类技术前沿（FW-HTF）项目开发的未来工作奖旨在利用新兴的机器人技术、人工智能和可穿戴传感器技术，使未来仓库和配送中心的叉车司机等工人受益。该奖项将支持收集初步数据和建立一个跨学科研究团队，以研究工作量和分心如何影响员工的表现，特别是这些影响对老年员工的影响。初步研究将确定适当的指标来量化变量，包括分心、舒适、性能、参与度和技术接受程度。研究将这些变量与非侵入性生理测量联系起来，包括眼动、心率、皮肤阻力、面部表情和脑电波。三个研讨会将帮助招募学术团队成员和行业利益相关者。参会的学术学科将包括物流、自主、软机器人、计算机视觉、学习科学、工业心理学、数据分析和机器学习、生物传感器和神经生理学、计算机网络安全和隐私、经济学和劳动力发展等领域的专家。将确定其他参与者，以补充和增强该团队目前来自达拉斯-沃斯堡地区物料搬运行业的工业合作者。该项目的结果将是一个由研究人员和利益相关者组成的综合跨学科团队，一套定义良好且有影响力的研究问题，以及解决这些问题的详细计划。最终，该项目将使运输物流部门及其工人受益，包括叉车和托盘千斤顶等车辆的操作员。更广泛地说，该研究项目将有助于创造技术干预措施，以减轻注意力分散，提高老龄化劳动力的绩效、安全性和生活质量，在各种职业中，微小的注意力分散可能导致涉及人身伤害、财产损失和操作中断的严重事故。例如无人机驾驶员、卡车司机、码头工人和船到岸起重机操作员。该项目包括参与和支持代表性不足的STEM研究人员的活动，包括研究生和本科生。该项目旨在验证和推进与叉车司机和其他工业操作员（IOs）主要注意力缺失相关的因素的解释框架，并进一步以人口和性别为特征。这个项目要解决的主要知识差距是从认知负荷、压力、疲劳、情绪刺激和计划等变量中识别出那些与注意力分散和危险的表现退化最密切相关的变量。试点数据将包括生理和心理测量。该项目将加深操作员对仓库和配送中心设置中先进技术的感知和互动，以减少干扰，提高性能和安全性。感兴趣的具体技术创新包括自主移动机器人的个性化交互规则和使用预测算法来指导IO行为。IO职业的长期职业可持续性将与年龄、教育程度、社会经济地位和性别等因素有关。其结果将改善工人的工作表现、舒适度和安全性，提高企业生产率，并减少因停机和受伤造成的成本。这项研究的广泛影响将被行业利益相关者的广泛参与放大。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This Future of Work at the Human Technology Frontier (FW-HTF) Project Development award addresses the use of emerging robotics, artificial intelligence, and wearable sensor technology to benefit workers such as forklift drivers in future warehouses and distribution centers. This award will support gathering of preliminary data and building of an interdisciplinary research team to study how workload and distractions affect worker performance, and in particular how these effects change for older workers. Preliminary studies will determine the appropriate metrics to quantify variables including distraction, comfort, performance, engagement, and levels of technology acceptance. Studies will relate these variables to noninvasive physiological measurements, including eye movement, heart rate, skin resistance, facial expressions, and brain waves. Three workshops will help recruit academic team members and industrial stakeholders. Academic disciplines represented will include experts in logistics, autonomous, soft robotics, computer vision, the science of learning, industrial psychology, data analytics and machine learning, biosensors and neurophysiology, computer network security and privacy, and economics and workforce development. Additional participants will be identified to complement and augment the team’s current industrial collaborators from the Dallas-Fort Worth area material handling industry. The result of this project will be an integrated interdisciplinary team of researchers and stakeholders, a set of well-defined and impactful research questions, and a detailed plan to address those questions. Ultimately the project will benefit the transportation logistics sector and its workers, including operators of vehicles such as forklifts and pallet jacks. More generally, the research project that is articulated under this Project Development award will help create technological interventions to mitigate distraction and improve performance, safety, and quality of life in an aging workforce, across various occupations where small distractions may lead to serious incidents involving physical injury, damage to property, and interruption of operations. Some examples are drone pilots, truck drivers, longshoremen, and ship-to-shore crane operators. This project includes activities to engage and support underrepresented STEM researchers at both graduate and undergraduate levels.This project seeks to validate and advance an explanatory framework of factors correlated to major attention lapses by forklift drivers and other industrial operators (IOs), further characterized by population and gender. The major knowledge gap to be addressed by this project is to identify from among variables such as cognitive load, stress, fatigue, emotional stimuli, and planning, those that correlate most strongly to distraction and dangerous degradation of performance. Pilot data will include physiological and psychological measures. This project will provide deeper understanding of operator perception of, and interaction with, advanced technology in warehouse and distribution center settings to mitigate distraction and improve performance and safety. Specific technology innovations of interest include personalized interaction rules for autonomous mobile robots and the use of predictive algorithms to guide IO behavior. The long-term career sustainability of IO occupations will be related to factors including age, education, socio-economic status, and gender. The results will improve worker performance, comfort, and safety, improve business productivity, and reduce costs due to downtime and injury. Broader impacts of this research will be amplified by the extensive involvement of industry stakeholders.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.