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

痴呆症的发病率有增加的趋势，特别是阿尔茨海默病。这个项目将调查新型交互计算技术的使用，例如人-机器人交互来收集生理和认知数据，以及数据分析的新方法，以回答一些有助于预防痴呆症的相互关联的问题。为了实现这一目标，该项目的第一部分将包括学习辅助社交机器人的最新技术：已经做了什么(经典的和最近的)，需要做什么，考虑到我的背景，我将如何做出贡献，从理论的角度我需要采取哪些额外的步骤。同时，承认和彻底理解这些程序的方法论方面将是该项目的关键。将采用一些机器学习和语音处理技术，如词性分析、潜在语义分析、主成分分析、贝叶斯概率方法等，并在使用不同编程语言的现有数据库上进行实践。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可能性，或者帮助我们比现在更早地将患者与健康对照组区分开来。特别是，我们可能会查看语音连贯指数来测试这一点，首先使用DementiaBank或Carolina Conversations Collection等现有数据集，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-机器人交互实验，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限制来设计实验，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中的实验。在第三部分，我们将记录大量的语音和手势数据，并使用在第一部分中测试的方法来分析这些数据。我们将反复改进机器人的交互模型，考虑到在实验期间收集的反馈，以改进用户参与度和必要的数据收集方法。

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.