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Human-Robot Interaction for dementia prevention and research with a focus on cognitive well-being

人机交互用于痴呆症预防和研究，重点关注认知健康

基本信息

批准号：
1805054
负责人：
金额：
--
依托单位：
University of Edinburgh
依托单位国家：
英国
项目类别：
Studentship
财政年份：
2016
资助国家：
英国
起止时间：
2016 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=studentship-1805054
关键词：
Human Robot Interaction dementia prevention

项目摘要

There is an increasing trend in the incidence of dementia, and, particularly Alzheimer's disease. This, along with its current prevalence and the socioeconomic burden it poses, is making its treatment and, even more, its prevention, a major worldwide public health challenge.This project will investigate the use of novel interactive computing technology, such as human-robot interaction to gather physiological and cognitive data, and new approaches for data analysis to answer a number of interrelated questions that will help dementia prevention. In order to accomplish this, the first part of the project will consist in learning the state-of-the-art on assistive social robotics: what has been done (classically and recently), what is needed, how would I contribute given my background, what extra steps would I need to take from a theoretical perspective. At the same time, acknowledging and thoroughly understanding the methodological side of these procedures will be key for the project. A number of machine learning and speech processing techniques such as part-of-speech analysis, latent semantic analysis, principal component analysis, Bayesian probability approaches, etc., will be introduced and practised on existing databases using different programming languages. These methods are expected to point out key features of participants' behaviour that may tell us their probability to suffer future dementia, or help us distinguish patients from healthy controls earlier than we do now. Particularly, we might look at speech coherence indexes to test this, first using existing datasets like DementiaBank or Carolina Conversations Collection, and afterwards, using our own data.The second part of the project will involve designing different experiments on human-robot interaction, gather relevant data and implement the methods mentioned above. For this, we will need to design the experiments according to ethical and clinical constrains, and then we will ask for appropriate advice and carefully decide which robotic platform would be suitable for the planned experiments. In the third part, we will record large amounts of speech and gesture data, and employ the methods tested during part 1 to analyse these data. We will refine the robot's interaction model iteratively, taking into account the feedback gathered during the experiments in order to improve user engagement and the data collection methods as necessary.

痴呆症的发病率呈上升趋势，特别是阿尔茨海默病。沿着其流行和社会经济负担，其治疗和预防已成为世界范围内的重大公共卫生挑战。本项目将探讨使用人机交互等新型交互式计算技术收集生理和认知数据，以及数据分析的新方法来回答一些有助于预防痴呆症的相关问题。为了实现这一目标，该项目的第一部分将包括学习辅助社会机器人的最新技术：已经做了什么（经典和最近），需要什么，我将如何贡献我的背景，我需要从理论的角度采取哪些额外的步骤。与此同时，承认和彻底理解这些程序的方法方面将是该项目的关键。许多机器学习和语音处理技术，例如词性分析、潜在语义分析、主成分分析、贝叶斯概率方法等，将使用不同的编程语言在现有的数据库上进行介绍和实践。这些方法有望指出参与者行为的关键特征，这些特征可能会告诉我们他们未来患痴呆症的概率，或者帮助我们比现在更早地区分患者和健康对照组。特别是，我们可以通过语音连贯性指数来测试这一点，首先使用现有的数据集，如DementiaBank或卡罗莱纳对话集，然后使用我们自己的数据。该项目的第二部分将涉及设计不同的人机交互实验，收集相关数据并实施上述方法。为此，我们需要根据伦理和临床约束设计实验，然后我们将征求适当的建议，并仔细决定哪个机器人平台适合计划的实验。在第三部分中，我们将记录大量的语音和手势数据，并使用第1部分中测试的方法来分析这些数据。我们将反复完善机器人的交互模型，考虑到实验期间收集的反馈，以提高用户参与度和必要的数据收集方法。