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

HomePal：开发基于智能扬声器的系统，用于老年人的家庭孤独评估

• 批准号: 10725229
• 负责人: Jane Chung
• 金额: $ 19万
• 依托单位: VIRGINIA COMMONWEALTH UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10725229
• 关键词: Acoustics; Activities of Daily Living; Adoption; Age; Algorithms; Awareness; Behavioral; Classification; Clinic; Communities; Computers; Data; Data Reporting; Detection; Devices; Digital biomarker; Dimensions; Effectiveness; Elderly; Enrollment; Frequencies; Future; Goals; Grain; Health; Health Personnel; Healthcare Systems; Home; Impaired cognition; Individual; Internet of Things; Intervention; Label; Life Expectancy; Linguistics; Loneliness; Measures; Mental Health; Monitor; Morbidity - disease rate; Music; Names; Outcome; Pain; Participant; Patient Self-Report; Patients; Pattern; Performance; Population; Primary Care; Privacy; Public Health; Reporting; Research; Resolution; Risk; Risk Factors; Security; Sleep; Social Interaction; Social Well-Being; Speech; Stream; System; Telephone; Testing; Time; Training; Work; community based service; community partnership; cultural values; dementia risk; digital; digital technology; experience; functional decline; generative adversarial network; implementation barriers; improved; innovation; insight; instrument; lonely individuals; machine learning algorithm; machine learning model; modifiable risk; mortality; novel; physical conditioning; prevent; recruit; sedentary lifestyle; smart speaker; social influence; social relationships; social stigma; therapy design; virtual

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.

项目摘要 孤独的经历在老年人中普遍存在。孤独是一种痛苦而有害的 当一个人的最佳社交互动水平与 实际的社会关系。鉴于孤独感与负面的健康结果有关 必须尽早确定孤独和高危个人以在逆境之前进行干预，这是必须采取的措施 发生健康结果。但是，由于存在的污名，对孤独的评估是具有挑战性的 被标记为孤独，缺乏将孤独评估整合到初级保健或基于社区的 服务。为了解决问题，我们将使用智能扬声器和被动灵敏度数据自动评估 老年人的孤独程度是在UCLA孤独量表上的自我报告分数相关的水平。我们的目标 该项目是开发，部署和验证基于智能扬声器的系统，用于家庭孤独感 整合物联网（IoT）设备以收集语音（声学和韵律）的评估 功能）和行为数据（例如，智能扬声器使用模式，家庭活动性，睡眠）来自老年人。我们 将招募70个人（65岁以上）独自生活在社区中，不断收集数字生物标志物数据 三个月。具体目的是：（1）开发一种创新的远程Loungess评估系统 可以在家庭环境中对语音和行为数据进行被动且不引人注目的捕获； （2）使用火车 - 测试方法，开发和评估新型多级机器学习（ML）算法的性能 - 半监督类型的生成对抗网络（SGANS） - 估计老年人的孤独感 分数。加州大学洛杉矶分校的孤独量表将由参与者每两周完成3个月来完成 地面真相数据； （3）确定在系统之间有效使用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，社会影响力， 文化价值以及可能影响采用和使用的虚拟药物的个性水平 系统。新颖的ML模型将使我们不仅可以识别“已经”孤独的人，而且可以识别“处于危险的人” 个人。拟议的研究旨在提供初步证据，表明SMART的数字生物标志物 演讲者和物联网设备可用于社区中的自动孤独感。结果 这项研究将支持我们纵向实施系统的长期目标，作为实时的平台 孤独评估和检测。这项工作将提供一个机会来研究 数字语音和行为数据以及孤独的自我报告措施，以识别可修改的风险因素 将告知设计干预措施，以防止孤独的不利影响并改善社会福祉 在老年人中。