SHB: Small: Socially Assistive Human-Machine Interaction for Improved Compliance and Health Outcomes

世界人口日益老龄化;据估计，到2050年，85岁以上的人口将比今天多三倍。许多人预计需要身体和认知方面的帮助，所有人都需要额外的医疗保健。初级保健医生，护士和管理式护理设施空间和工作人员的短缺已经成为当今的一个问题，为辅助技术填补护理空白创造了一个利基市场。越来越多的研究表明，某些行为模式对长寿和健康有积极影响，包括定期体育锻炼，社交互动和认知参与。本项目旨在开发和评估社会辅助人机交互技术，影响用户参与和遵守家庭或养老院的健康促进行为，以提高寿命和生活质量。这项工作的重点是开发社会辅助系统（SAS），使用对等水平的人-机交互技术，机器交互，其中机器充当知识渊博的具体代理人的角色，能够提供时间延长的、持续的和吸引人的交互。该研究主要集中在两种类型的健康促进人机交互：1）锻炼课程（认知和/或身体）和2）社交课程。 对于一对一的锻炼课程，该项目正在开发SAS功能，提供锻炼监控，指导和激励。 对于社会化，该研究正在开发SAS功能，提供社交互动和友好的提醒和鼓励，以遵守健康方案（服药，体育活动）和健康习惯（打电话给家人，会见朋友）。 这两种类型的交互有着共同的目标，即影响人类行为，研究的主要贡献是通过人机交互影响行为的方法和算法。这是通过一种新颖的双重方法来实现的：1）针对短期交互的自适应交互转向，以及2）在长期交互中对行为进行激励和辅导，以保持参与度并提高任务绩效。SAS的实施被用来最大限度地提高参与度和合规性;工作的重点是开发一种综合方法，用于自然，有说服力和参与性的具体沟通。正在开发的方法和算法是通用的，将在基于计算机和基于机器人的代理上实现和测试。 以这种方式，该工作在多个技术平台和实施例上验证所开发的方法，并比较它们的相对有效性和预期用户群体的可接受性。实施的细节是由一个早期的重点小组与老年参与者，目标用户群体。 所开发的技术正在实现人机交互（HCI）和人机交互（HRI）的版本，并正在评估与SAS实现的一个多星期的用户研究与一个大的老年用户群。 这项工作的重点是生成方法，算法和大量的多模态数据（视频，音频和调查问卷），用于继续研究健康和健康相关的HRI和HCI。 该项目旨在解决国家公认的促进老龄人口健康和长寿的医疗保健挑战的一个组成部分。 除了基本的算法和方法开发，该项目还实施和测试了现实世界的社会辅助系统，包括基于计算机和基于机器人的系统，以及大量的老年退休家庭居民。 该项目预计将为研究和医疗保健产品开发提供有用的见解，以解决这一不断增长的人口群体。 除了社会相关的研究重点，该项目团队还参与了K-12外展活动的综合计划，该计划使用机器人技术来促进STEM主题学习。 该项目涉及内城K-12教师和学生在年度开放日，集会和研讨会，为老年人提供辅助系统的实践经验，以及带回家继续学习的材料。 该项目团队包括博士生，本科生和K-12志愿者，并建立了一个指导管道，使大学生既得到指导，又成为年轻同龄人的导师和榜样。

The world's population is growing older; it is estimated that in 2050 there will be three times more people over the age 85 than there are today. Many are expected to need physical and cognitive assistance, and all will need additional healthcare. Shortages in primary care physicians, nurses, and managed care facility space and staff are already an issue today, creating a niche for assistive technologies to fill the care gap. A growing body of research shows that certain behavior patterns have a positive impact on longevity and wellness, including regular physical exercise, social interaction, and cognitive engagement. This project aims to develop and evaluate socially assistive human-machine interaction techniques that influence the user to engage in and comply with wellness-promoting behaviors in the home or nursing home in order to enhance longevity and quality of life.This work is focused on developing socially assistive systems (SAS) using peer-level human-machine interaction in which the machine serves in the role of a knowledgeable embodied agent capable of providing time-extended, sustained, and engaging interaction. The research focuses on two types of wellness-promoting human-machine interactions: 1) exercise sessions (cognitive and/or physical) and 2) socializing sessions. For one-on-one exercise sessions, the project is developing SAS capabilities that provide exercise monitoring, coaching, and motivation. For socialization, the research is developing SAS capabilities that provide social interaction and friendly reminders and encouragement to comply with health regimens (taking medicine, being physically active) and healthy habits (calling family, meeting friends). The two types of interactions share the common goal of influencing human behavior, and the main contribution of the research is the set of methods and algorithms for influencing behavior though human-machine interaction. This is achieved through a novel two-fold approach: 1) adaptive interaction steering for short-term interactions, and 2) motivation and coaching of behavior in longer-term interactions to maintain engagement and enhance task performance. The embodiment of the SAS is leveraged to maximize engagement and compliance; a key focus of the work is on developing a comprehensive method for natural, persuasive and engaging embodied communication. The methods and algorithms being developed are general, and will be implemented and tested on both computer-based and robot-based agents. In this way the work validates the developed methods on multiple technology platforms and embodiments, and compares their relative effectiveness and acceptability by the intended user population. The specifics of the implementation are informed by an early focus group with elderly participants, the target user population. The developed techniques are being implemented in both human-computer interaction (HCI) and human-robot interaction (HRI) versions, and are being evaluated with a large group of elderly users interacting with the SAS implementations over a multi-week user study. The work is focused on generating methods, algorithms, and a large corpus of multi-modal data (video, audio, and questionnaires) for grounding continued research into health and wellness-relevant HRI and HCI. This project aims to address a component of the nationally recognized healthcare challenge of promoting wellness and longevity in the aging population. Beyond basic algorithm and method development, the project implements and tests real-world socially assistive systems, both computer-based and robot-based, with a large population of elderly retirement home residents. The project is expected to produce insights useful for both research and healthcare product development for addressing this growing segment of the population. In addition to the socially relevant research focus, the project team is also engaged in a comprehensive program of K-12 outreach activities, which use robotics to promote STEM topic learning using the health theme of the proposal. The project involves inner city K-12 teachers and students in annual open houses, assemblies, and workshops that provide hands-on experiences with assistive systems for the elderly, as well as take-home materials for continued learning. The project team includes PhD students, undergraduates, and K-12 volunteers, and establishes a mentoring pipeline so that university students are both mentored and serve as mentors and role models for their younger peers.