Artificial Intelligence Applied to Video and Speech for Objectively Evaluating Social Interaction and Depression in Mild Cognitive Impairment

人工智能应用于视频和语音，客观评估轻度认知障碍患者的社交互动和抑郁情况

• 批准号: 10810965
• 负责人: Gari David Clifford
• 金额: $ 43.04万
• 依托单位: EMORY UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-25 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10810965
• 关键词: Acoustics; Aging; Alzheimer' s Disease; Area; Artificial Intelligence; Behavior; Behavioral; Behavioral Research; Biological Markers; Bluetooth; Characteristics; Clinical Trials; Cognitive; Communication; Communities; Complex; Cues; Data; Dimensions; Disease; Early Diagnosis; Elderly; Emotions; Ethnic Origin; Expert Systems; Face; Facial Expression; Feeling; Filler; Generations; Goals; Health; Health behavior; Healthcare; Human; Impaired cognition; Individual; Interview; Life; Linguistics; Loneliness; Longitudinal Studies; Measurement; Measures; Mental Depression; Mental Health; Mission; Monitor; Outcome; Participant; Patient Monitoring System; Patients; Personal Satisfaction; Persons; Population; Process; Psyche structure; Public Health; Race; Reporting; Research; Services; Social Behavior; Social Distance; Social Interaction; Social isolation; Social support; Speech; Structure; Surveys; System; Techniques; Technology; Technology Assessment; Therapeutic; Training; Training Programs; Validation; Video Recording; Work; behavioral impairment; cognitive testing; cognitive training; cost; empowerment; ethnic diversity; evidence base; experience; feature detection; geriatric depression; irritation; microphone; mild cognitive impairment; multimodality; novel; prevent; privacy preservation; programs; public health relevance; recruit; screening; sensor; social; social engagement; standard measure; theories; vocal cord

Project Summary/Abstract The decreases in social engagement and the feeling of depression and loneliness are reported to be highly correlated with the progression of mild cognitive impairment (MCI). However, previous studies primarily relied on retrospective survey analysis, which lacks continuously quantifying such behavioral biomarkers in the real world. Recent advances in artificial intelligence (AI) made it capable of assessing human physical and mental activities in the real world. Yet, those techniques are mainly evaluated on normal populations, whose behaviors are distinctively different from MCI individuals. This proposal aims to validate AI technologies applied to video and speech for objectively evaluating social interaction and mental health in a MCI population. Successful completion of this proposal will provide an important step toward our long-term goal, which is a large-scale longitudinal study for continuous, objective quantification of MCI progression in the real world. Our general hypothesis is that the subtle yet important changes in social engagement and mental health during real-world interactions of MCI individuals can be continuously captured and quantified for cognitive impairment. For our study, MCI patients will be recruited with balanced race and ethnic backgrounds from the urban Atlanta, GA, area. In Aim 1, social engagement analysis will be conducted at Cognitive Empowerment Program (CEP) at Emory Goizueta Alzheimer’s Disease Research Center. The CEP space is installed with an edge computing-based, privacy- preserving, low-cost patient monitoring system having multi-modal sensors including cameras. At CEP, the recruited MCI patients will be participating in physical and cognitive training programs provided by therapeutic service professionals. MCI patients' social engagement features detected from the multi-camera network system will be used to predict the Perceived Social Support Scale (PSS) and Montreal Cognitive Assessment (MoCA) scores of corresponding patients. In Aims 2 and 3, depression and loneliness analysis is conducted with video recordings of the Cognitive Assessment Interview (CAI) from the recruited MCI patients. For gold standard depression and loneliness scales, MCI participants will provide Geriatric Depression (GDS) and UCLA Loneliness scales, respectively. From the video recordings, the facial (Aim 2) and speech (Aim 3) behavior of MCI patients will be analyzed for the prediction of GDS, UCLA Loneliness, and MoCA scales. The validation of techniques in this proposal is expected to have a significant impact on quantifying MCI progressions as these techniques can be readily extended to quantifying other MCI-related behaviors in the real world, such as wandering or apathy. Also, this proposal’s findings in social and mental health features can drive novel hypothesis generation to power clinical trials for developing novel treatments for cognitive decline. This research aligns with the NIA’s mission to understand the diseases and conditions associated with the aging process in order to extend healthy years of life by supporting behavioral research on aging and disseminating research information to the scientific community.

项目概要/摘要 据报道，社交参与度的下降以及抑郁和孤独的感觉非常严重。 与轻度认知障碍（MCI）的进展相关然而，先前的研究主要依赖于。 基于回顾性调查分析，缺乏对真实情况中此类行为生物标志物的持续量化 人工智能（AI）的最新进展使其能够评估人类的身体和心理。 然而，这些技术主要针对正常人群的行为进行评估。 与 MCI 个人明显不同。该提案旨在验证应用于视频的人工智能技术。 以及客观评估 MCI 人群的社交互动和心理健康的演讲。 该提案的完成将为我们的长期目标迈出重要一步，即大规模 对现实世界中 MCI 进展进行连续、客观量化的纵向研究。 假设是现实世界中社会参与和心理健康发生微妙但重要的变化 对于我们的认知障碍，MCI 个体的相互作用可以被持续捕获和量化。 研究中，将从佐治亚州亚特兰大市招募具有均衡种族和民族背景的 MCI 患者， 在目标 1 中，社会参与分析将在埃默里大学的认知赋权计划 (CEP) 中进行。 Goizueta 阿尔茨海默病研究中心 CEP 空间安装了基于边缘计算的隐私- 在 CEP，具有多模态传感器（包括摄像头）的低成本患者监护系统。 招募的 MCI 患者将参加治疗机构提供的身体和认知训练计划 从多摄像头网络系统检测到 MCI 患者的社交参与特征。 将用于预测感知社会支持量表（PSS）和蒙特利尔认知评估（MoCA） 在目标2和目标3中，通过视频进行抑郁和孤独感分析。 招募的 MCI 患者的认知评估访谈 (CAI) 录音为金标准。 抑郁和孤独感量表，MCI 参与者将提供老年抑郁症 (GDS) 和加州大学洛杉矶分校孤独感量表 分别从视频记录中观察 MCI 患者的面部（目标 2）和言语（目标 3）行为。 将分析 GDS、UCLA 孤独感和 MoCA 量表的预测，并验证技术。 该提案预计将对量化 MCI 进展产生重大影响，因为这些技术可以 很容易扩展到量化现实世界中其他与 MCI 相关的行为，例如徘徊或冷漠。 该提案在社会和心理健康特征方面的发现可以推动新假设的产生 开发治疗认知能力下降的新疗法的临床试验与 NIA 的使命相一致。 了解与衰老过程相关的疾病和状况，以延长健康寿命 通过支持衰老行为研究并向科学界传播研究信息。