ATSA-ESI: Assistive Technology Supporting Aging with Emotional and Social Intelligence

在过去的12年里，我一直在构建复杂的辅助技术(AT)，旨在帮助患有痴呆症(如阿尔茨海默氏症)的老年人。日益老龄化的人口给医疗保健系统带来了巨大的压力，给非正式照顾者(例如，配偶/子女在家照顾配偶/父母)带来了不可持续的负担。我开发的技术可以在衰老过程中提供自主性和独立性，从而将非正式护理人员从部分负担中解放出来，并降低医疗成本。*已经取得了很大进展，但在这些设备有效和可用之前，还有很长的路要走。这项建议旨在建立一个统一的操作模式，从工程学、心理学和社会学的角度对老年护理辅助技术(AT)进行研究。社交、情绪和情感智力的概念在追求有效的人机交互方面发挥着越来越重要的作用，包括AT。对于AT的发展，众所周知，这些概念对痴呆症患者的行为有重大影响。例如，由于阿尔茨海默氏症干扰记忆，它通常会导致一个人实施过去的身份，而治疗这样的人会造成不和谐，导致冲突、压力和抑郁，当然还会导致AT不收养。为了解决这个问题，我开发了人工智能系统，将社会和情感因素整合到包括AT在内的人与人互动的系统中。我使用概率和决策理论序列动力学模型的基础来开发这些系统。*我未来五年的研究将集中在群体过程模型的理论和实践发展上，以社会心理学理论为指导，并具体应用于AT的老龄化。我将沿着三条主要途径工作：对个人群体中的情绪进行分层建模，并应用于特定的辅助技术；从个人和群体互动的传感器数据中对用户和上下文模型进行机器学习；以及基于社会心理学原理，通过应用于社交网络，开发超越AT的一般群体过程模型。我目前支持的研究具体着眼于个人互动层面的社会情感问题(例如，一个人参与了一项单一活动)，并以用户评估为重点。在这项建议中，我要解决的是模范个人群体的问题，超越了需要照顾的人，包括他们的非正式照顾者、他们的医生、专业照顾者、子女、兄弟姐妹、邻居和社区。基于社会心理理论，我将开发和测试老年人的社会网络模型，以提供更有效和量身定做的照顾。这项工作将与我的其他拨款资助的另一项更注重个人的工作相吻合。**

For the past twelve years I have been building sophisticated assistive technology (AT) designed to assist elderly persons with dementia (e.g. Alzheimer's). An increasingly elderly population is placing a significant strain on the healthcare system, and an unsustainable burden on informal caregivers (e.g. spouses/children caring for their spouse/parent at home). I develop technologies that can provide autonomy and independence in aging, thereby releasing informal carers from part of this burden, and decreasing healthcare costs.******Much progress has been made, but there is a long way to go before these devices are effective and usable. This proposal is to build a unified operational model of engineering, psychological and sociological perspectives on assistive technology (AT) for elder care. Increasingly, the notions of social, emotional and affective intelligence are playing a key role in the pursuit of effective human-machine interaction, including AT. For AT development, these notions are known to have a major impact on the actions of persons with dementia. For example, as Alzheimer's disease interferes with memory, it can often result in a person enacting a past identity, and treating such a person otherwise creates dissonance leading to conflict, stress, and depression, and certainly to AT non-adoption. To handle this, I develop artificially intelligent systems that integrate social and emotional factors into human-interactive systems including AT. I develop these systems using a foundation of probabilistic and decision theoretic sequential dynamical models. ******The next five years of my research will focus on the theoretical and practical development of group process models, guided by social-psychological theory, with specific application to AT for aging. I will work along three key avenues: hierarchical modelling of sentiment in groups of individuals and applications to specific assistive technologies, machine learning of user and context models from sensor data of individual and group interactions, and development of models of group processes in general beyond AT, based on social-psychological principles, with application to social networks. My currently supported research looks specifically at socio-emotional issues at the level of individual interactions (e.g. a single individual engaged in a single activity), and with a user-evaluative focus. In this proposal, I tackle the problem of modelling groups of individuals, going beyond the person in need of care to include their informal caregiver, their doctor, professional carer, offspring, siblings, neighbours and community. Based on social-psychological theory, I will develop and test models of social networks of elderly persons to provide more effective and tailored care. This work will dovetail with the other, more individual-oriented work funded by my other grants.**