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

项目摘要 孤独感在老年人中很普遍。寂寞是一种痛苦的， 当一个人的最佳社会交往水平与 真正的社会关系。鉴于孤独与负面的健康结果有关， 必须采取措施及早识别孤独和高危人群，以便在不良反应发生之前进行干预。 健康结果发生。然而，对孤独的评估是具有挑战性的，因为被认为是一种耻辱。 标记为孤独和缺乏整合的孤独评估到初级保健或社区为基础的 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 老年人的孤独水平与UCLA孤独量表的自我报告分数相关。我们的目标 该项目旨在开发、部署和验证一种基于智能扬声器的系统，以应对家庭孤独感 集成物联网（IoT）设备以收集语音（声学和韵律）的评估 特征）和行为数据（例如，智能扬声器使用模式、家庭移动性、睡眠）。我们 将招募70名独居社区的个体（65岁以上），以持续收集数字生物标志物数据 3个月具体目标是：（1）开发一个创新的远程孤独评估系统， 允许在家庭环境中被动和不显眼地捕获语音和行为数据;（2）使用训练- 测试方法，开发和评估新型多类机器学习（ML）算法的性能- 半监督型生成对抗网络（SGAN）-估计老年人的孤独感 成绩.参与者将每两周填写一次UCLA孤独量表，持续3个月， 地面实况数据;以及（3）确定有效使用该系统的潜在实施障碍， 老年人（n=30）。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水平，这可能会影响采用和使用 系统新的机器学习模型将使我们不仅能够识别“已经”孤独的人，而且还能识别“处于危险之中”的人。 个体这项拟议中的研究旨在提供初步证据，证明智能生物标志物 扬声器和物联网设备可用于社区中的自动孤独评估。结果 这项研究将支持我们的长期目标，即纵向实施该系统作为一个实时平台， 孤独感评估和检测。这项工作将提供一个机会，审查之间的联系， 数字语音和行为数据以及孤独感的自我报告措施，以确定可改变的风险因素， 将为干预措施的设计提供信息，以防止孤独对健康的不利影响，并改善社会福祉， 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.