PFI:BIC - Adaptive Robotic Nursing Assistants for Physical Tasks in Hospital Environments

PFI:BIC - 在医院环境中执行体力任务的自适应机器人护理助理

• 批准号: 1643989
• 负责人: Dan Popa
• 金额: $ 86.32万
• 依托单位: University of Louisville Research Foundation Inc
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-06-01 至 2019-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1643989&HistoricalAwards=false
• 关键词: PFI; BIC; Adaptive; Robotic; Nursing

This Partnerships for Innovation: Building Innovation Capacity (PFI:BIC) project aims to provide next-generation assistive robots to support the activities of hospital-based registered nurses (RNs). There are nearly three million registered nurses employed in the United States, making them the largest pool of healthcare providers in the country. Technology that affects the performance of this large labor pool cannot fail to have impact. Due to advancements in robotics and computer technology, access to intelligent communication, sensing, and computing hardware is on the cusp of becoming common--not only for healthcare professionals, but also for patients themselves. The project led by The University of Kentucky at Louisville in collaboration with the University of Texas at Arlington will focus on the creation of new design tools that can configure the hardware and software of adaptive robotic nursing assistants (ARNA). ARNA will be specifically designed to assist nurses in healthcare facilities with simple tasks such as, lift assistance, delivery of everyday lightweight objects (medicine, medical wearable equipment), and some physical assistance with movement of heavier objects, such as furniture, gurneys, and the patients themselves. The design and engineering innovations resulting from insights gained in this project may have great value deployed as products in broader consumer markets in addition to hospitals. Examples include in-home service and assistive robots, robots for assistance in public venues, and co-Robot manufacturing where humans are in close proximity to robot workers. The improved understanding of human-robot and nurse-robot interaction could represent enabling technology that will facilitate research breakthroughs and increase productivity and social acceptance of robotics. The research will also advance the understanding of the perceptual effects of robot design aesthetics and interfaces.The proposed Adaptive Robotic Nurse Assistants will navigate cluttered hospitals, while equipped with multi-modal skin sensors that can anticipate nurse intent, automate mundane low-level tasks, but keep nurses in the decision loop. Modular and strong hardware will be deployed in reconfigurable platforms specially designed for nurse physical assistance. Adaptive human-machine interfaces will play a key role in this project, as these interfaces directly impact the ability of robots to help nurses in a dynamic, unstructured environment. Rather than pre-programming robot behaviors, learning algorithms will be used so that robots adapt to human preferences. Two leading applications are envisioned for feasibility evaluation by quantitative and qualitative metrics, including patient sitters and walkers. The sitter robot will take vital sign measurements, evaluate risk from patient movement and pose, and provide continuous observation of patients and feedback to and from nurses. The walker robot will assist nurses and patients by providing partial balance support, navigating cluttered environments, and assisting with medical equipment transportation.The lead institution is the University of Kentucky at Louisville in collaboration with the University of Texas at Arlington with its multidisciplinary departments including the College of Engineering, College of Nursing, and the University of Texas at Arlington Research Institute (UTARI). Primary industrial partners include QinetiQ-North America (Waltham, MA), a large corporation specializing in unmanned systems, and RE2 (Pittsburgh, PA), a small business specializing in modular robotic manipulators that will contribute unique battle-tested hardware and systems engineering. In-hospital testing and evaluation of the proposed robots will be carried out by nurse researchers at the University of Texas at Arlington College of Nursing and Texas Health Resources (Dallas-Fort Worth, TX), a large healthcare provider.

这种创新伙伴关系：创新能力建设（PFI：BIC）项目旨在提供下一代辅助机器人，以支持医院注册护士（RN）的活动。在美国有近300万注册护士，使他们成为该国最大的医疗保健提供者。影响这一庞大劳动力资源的技术必然会产生影响。由于机器人技术和计算机技术的进步，智能通信、传感和计算硬件的使用正变得越来越普遍--不仅对医疗保健专业人员，而且对患者本身也是如此。该项目由位于路易斯维尔的肯塔基州大学与位于阿灵顿的德克萨斯大学合作领导，将专注于创建新的设计工具，可以配置自适应机器人护理助理（ARNA）的硬件和软件。ARNA将专门设计用于帮助医疗机构的护士完成简单的任务，例如升降辅助，日常轻型物体（药品，医疗可穿戴设备）的交付，以及一些较重物体（如家具，轮床和患者本身）的移动物理辅助。从该项目中获得的见解所产生的设计和工程创新可能具有巨大的价值，除了医院之外，还可以作为更广泛的消费者市场的产品部署。例子包括家庭服务和辅助机器人，公共场所的辅助机器人，以及人类与机器人工人非常接近的合作机器人制造。对人与机器人和护士与机器人互动的更好理解可能代表了促进研究突破、提高机器人生产力和社会接受度的技术。该研究还将推进对机器人设计美学和界面的感知效果的理解。拟议的自适应机器人护士助理将在混乱的医院中导航，同时配备多模态皮肤传感器，可以预测护士的意图，自动化日常低级别任务，但让护士参与决策循环。模块化和强大的硬件将部署在可重新配置的平台，专门为护士的身体援助。自适应人机界面将在该项目中发挥关键作用，因为这些界面直接影响机器人在动态非结构化环境中帮助护士的能力。而不是预先编程机器人的行为，学习算法将用于使机器人适应人类的喜好。两个领先的应用程序设想的可行性评估的定量和定性指标，包括病人坐着和步行。保姆机器人将进行生命体征测量，评估患者运动和姿势的风险，并提供对患者的持续观察和护士的反馈。该步行者机器人将通过提供部分平衡支持、在杂乱的环境中导航以及协助医疗设备运输来帮助护士和患者。牵头机构是位于路易斯维尔的肯塔基州大学与位于阿灵顿的德克萨斯大学及其多学科部门合作，包括工程学院、护理学院和位于阿灵顿的德克萨斯大学研究所（UTARI）。主要的工业合作伙伴包括QinetiQ-North America（马萨诸塞州沃尔瑟姆），一家专门从事无人驾驶系统的大公司，以及RE 2（宾夕法尼亚州匹兹堡），一家专门从事模块化机器人操纵器的小企业，将提供独特的经过战斗考验的硬件和系统工程。对拟议机器人的医院测试和评估将由德克萨斯大学阿灵顿护理学院和德克萨斯州卫生资源（达拉斯-沃斯堡，德克萨斯州）的护士研究人员进行，这是一家大型医疗保健提供商。