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

这一创新伙伴关系：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300万注册护士受雇，使他们成为美国最大的医疗保健提供者群体。影响这一庞大劳动力池表现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、传感和计算硬件即将变得普遍--不仅对医疗专业人员，而且对患者本身也是如此。该项目由德克萨斯大学阿灵顿分校领导，将专注于创造新的设计工具，可以配置自适应机器人护理助理(ARNA)的硬件和软件。Arna将专门设计为帮助医疗设施中的护士完成简单的任务，如举起辅助、日常轻便物品(药品、医疗可穿戴设备)的运送，以及一些移动较重物品(如家具、轮床和患者本身)的物理协助。从这个项目中获得的洞察力所产生的设计和工程创新可能会作为产品部署在除医院之外的更广泛的消费市场上。例子包括居家服务和辅助机器人，在公共场所协助的机器人，以及人类靠近机器人工人的联合机器人制造。对人-机器人和护士-机器人互动的理解的提高可能代表着使能技术将促进研究突破，并提高生产力和社会对机器人的接受度。这项研究还将促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，同时配备多模式皮肤传感器，可以预测护士的意图，自动化平凡的低级别任务，但使护士处于决策循环中。模块化和强大的硬件将部署在专门为护士物理辅助而设计的可重新配置的平台中。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。设想了两个领先的应用程序，用于通过定量和定性指标进行可行性评估，包括患者坐位器和助行器。看护机器人将测量生命体征，评估患者移动和姿势的风险，并提供对患者的持续观察和护士的反馈。步行者机器人将通过提供部分平衡支持、在混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。牵头机构是德克萨斯大学阿灵顿分校，其多学科系包括工程学院、护理学院和德克萨斯大学阿灵顿研究所(UTARI)。主要的工业合作伙伴包括QinetiQ-North America(马萨诸塞州沃尔瑟姆)和RE2(宾夕法尼亚州匹兹堡)，前者是一家专门生产无人系统的大公司，后者是一家专门生产模块化机器人操作器的小企业，它将贡献经过战斗考验的独特硬件和系统工程。拟议中的机器人的医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健康资源公司(达拉斯-沃斯堡，德克萨斯州)的护士研究人员进行，该公司是一家大型医疗保健提供商。

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 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 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.