HomePal: Developing a Smart Speaker-Based System for In-Home Loneliness Assessment for Older Adults

项目摘要 孤独的经历在老年人中很普遍。孤独是一种痛苦和有害的 当一个人的社会互动的最佳水平与 实际的社会关系。鉴于孤独一直与负面的健康后果有关，检测 必须采取措施，及早识别孤独和有风险的个人，以便在不利的情况下进行干预 健康后果就会发生。然而，由于孤独的耻辱，评估孤独是具有挑战性的 被贴上孤独的标签，以及缺乏将孤独评估纳入初级保健或社区 服务。为了解决这个问题，我们将使用智能扬声器和被动传感数据来自动评估 老年人的孤独程度与加州大学洛杉矶分校孤独量表上的自我报告分数相关。我们的目标是 这个项目是开发，部署和验证一个基于智能扬声器的系统，用于家庭孤独 集成物联网(IoT)设备以收集语音(声学和韵律)的评估 特征)和来自老年人的行为数据(例如，智能扬声器的使用模式、在家中的移动性、睡眠)。我们 将招募70名单独居住在社区的个人(65岁以上)，以持续收集数字生物标志物数据 三个月。具体目标是：(1)开发一种创新的远程孤独评估系统 允许被动和不引人注意地捕获家庭环境中的语音和行为数据；(2)使用列车- 测试方法，开发和评估新的多类机器学习(ML)算法的性能- 半监督型生成性对抗网络(SGANs)--用于评估老年人的孤独感 得分。加州大学洛杉矶分校的孤独量表将由参与者在3个月内每两周完成一次 实地实况数据；和(3)查明有效使用该系统的潜在执行障碍 老年患者30例。特别是，我们将评估潜在的隐私和安全问题、社会影响、 文化价值观，以及虚拟代理人的人格化程度，这可能会影响对 系统。新颖的ML模型将使我们不仅可以识别“已经”孤独的人，而且还可以识别“处于危险中”的人 个人。这项拟议的研究试图提供初步证据，证明来自SMART的数字生物标记物 音箱和物联网设备可以用于社区中的自动孤独评估。结果来自 这项研究将支持我们的长期目标，即作为一个实时平台纵向实施该系统 孤独感评估和检测。这项工作将提供一个机会来研究 数字言语和行为数据以及孤独的自我报告测量，以确定可修改的风险因素， 将为设计干预措施提供信息，以防止孤独对健康的不利影响，并改善社会福利- 在老年人口中。

Project Abstract Experiences of loneliness are prevalent among older adults. Loneliness is a painful and pernicious state occurring when there is a perceived discrepancy between one’s optimal levels of social interactions and actual social relationships. Given that loneliness has been associated with negative health outcomes, detection measures are imperative to identify both lonely and at-risk individuals early enough to intervene before adverse health outcomes occur. However, the assessment of loneliness is challenging due to the stigma of being labeled as lonely and the lack of integration of loneliness assessments into primary care or community-based services. To tackle the problem, we will use smart speakers and passive sensing data to automatically assess older adults’ level of loneliness as correlates of self-report scores on the UCLA Loneliness Scale. Our goal in this project is to develop, deploy, and validate a smart speaker-based system for in-home loneliness assessment that integrates Internet of Things (IoT) devices to gather both speech (acoustic and prosodic features) and behavioral data (e.g., smart speaker use patterns, in-home mobility, sleep) from older adults. We will enroll 70 individuals (age 65+) living alone in the community to collect digital biomarker data continuously for 3 months. The specific aims are to: (1) Develop an innovative remote loneliness assessment system that allows passive and unobtrusive capture of speech and behavioral data in the home setting; (2) Using a train- test approach, develop and evaluate the performance of novel multi-class machine learning (ML) algorithms— semi-supervised type of Generative Adversarial Networks (SGANs) — to estimate older adults’ loneliness scores. The UCLA Loneliness Scale will be completed by participants every two weeks for 3 months to collect ground truth data; and (3) Identify potential implementation barriers to the effective use of the system among older adults (n=30). In particular, we will assess potential privacy and security concerns, social influence, cultural values, and the level of personification of virtual agents that could influence the adoption and use of the system. The novel ML models will allow us to identify not only “already” lonely individuals but also “at-risk” individuals. The proposed research seeks to provide preliminary evidence that digital biomarkers from smart speakers and IoT devices can be used for automatic loneliness assessment in the community. Results from this study will support our long-term goal of implementing the system longitudinally as a platform for real-time loneliness assessment and detection. This work will provide an opportunity to examine associations between digital speech and behavioral data and self-report measures of loneliness to identify modifiable risk factors that will inform the design of interventions to prevent adverse health impacts of loneliness and improve social well- being among the older adult population.