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.

该创新伙伴关系：建设创新能力 (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。美国有近三百万注册护士，是美国最大的医疗保健提供者群体。影响这个庞大劳动力库绩效的技术不可能不产生影响。由于机器人和计算机技术的进步，智能通信、传感和计算硬件的使用即将变得普遍——不仅对医疗保健专业人员来说，而且对患者本身来说也是如此。该项目由肯塔基大学路易斯维尔分校与德克萨斯大学阿灵顿分校合作领导，重点是创建新的设计工具，可以配置自适应机器人护理助理（ARNA）的硬件和软件。 ARNA 将专门设计用于协助医疗机构的护士完成简单的任务，例如举升协助、运送日常轻量物体（药品、医用可穿戴设备），以及移动较重物体（例如家具、轮床和医疗设备）的一些身体协助。患者本人。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，可以作为产品部署到除医院之外的更广泛的消费市场。例如，家庭服务和辅助机器人、公共场所协助机器人以及人类与机器人工人近距离接触的协作机器人制造。对人类与机器人和护士与机器人交互的理解的加深可能代表着促进研究突破、提高生产率和社会对机器人技术的接受度的技术。该研究还将增进对机器人设计美学和界面感知效果的理解。拟议的自适应机器人护士助理将在杂乱的医院中导航，同时配备多模态皮肤传感器，可以预测护士的意图，自动执行日常的低级任务，但让护士参与决策。模块化且强大的硬件将部署在专为护士身体援助而设计的可重构平台中。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。将使用学习算法，而不是预先编程机器人的行为，以便机器人适应人类的偏好。设想通过定量和定性指标进行可行性评估的两个主要应用，包括病人看护者和步行者。坐式机器人将进行生命体征测量，评估患者运动和姿势的风险，并持续观察患者并向护士提供反馈。步行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。牵头机构是肯塔基大学路易斯维尔分校，与德克萨斯大学阿灵顿分校及其多学科部门合作，包括工程学院、护理学院和德克萨斯大学阿灵顿研究所 (UTARI)。主要工业合作伙伴包括 QinetiQ-North America（马萨诸塞州沃尔瑟姆），一家专门从事无人系统的大型公司，以及 RE2（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型企业，该公司将提供经过实战考验的独特硬件和系统工程。对拟议机器人的院内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健康资源公司（德克萨斯州达拉斯沃斯堡）的护士研究人员进行，这是一家大型医疗保健提供者。