SCH: INT: Collaborative Research: Exploiting Voice Assistant Systems for Early Detection of Cognitive Decline

SCH：INT：合作研究：利用语音辅助系统早期检测认知衰退

• 批准号: 10019452
• 负责人: Xiaohui Liang
• 金额: $ 29.61万
• 依托单位: UNIVERSITY OF MASSACHUSETTS BOSTON
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-30 至 2023-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10019452
• 关键词: Address; Alzheimer' s disease related dementia; Caregivers; Characteristics; Clinic; Cognition; Cognitive; Communication; Complement; Consumption; Custom; Data; Databases; Diagnostic; Early Diagnosis; Elderly; Evaluation; Home environment; Human; Impaired cognition; Individual; Intervention; Knowledge; Learning; Link; Measures; Methods; Modality; Modeling; Nature; Neural Network Simulation; Outcome; Participant; Patients; Pattern; Population; Psychological Transfer; Quality of life; Research; Sampling; Series; Speech; Structure; Support System; System; Techniques; Time; Visit; Voice; base; cognitive testing; cost; data mining; data quality; improved; novel; predictive modeling; recurrent neural network; screening

Early detection of the cognitive decline involved in Alzheimer's Disease and Related Dementias (ADRD) in older adults living alone is essential for developing, planning, and initiating interventions and support systems to improve patients' everyday function and quality of life. Conventional, clinic-based methods for early diagnosis are expensive, time-consuming, and impractical for large-scale screening. This project aims to develop a low-cost, passive, and practical home-based assessment method using Voice Assistant Systems (VAS) for early detection of cognitive decline, including a set of novel data mining techniques for sparse time-series speech. The project has three specific aims: 1. Using a recurrent neural network (RNN) and a softmax regression model, we will develop a transfer learning technique to investigate the link between the speech from in-lab VAS tasks and cognitive decline. The Pitt corpus from the DementiaBank database will be used to optimize the RNN parameters and thereby overcome the limited data problem of VAS. The softmax regression model will allow us to align the feature distributions from the previous speech data and in-lab VAS speech. 2. We will develop a novel "many-to-difference" prediction model with a symmetric RNN structure to predict the cognitive difference at two ends of a time period from the sparse time-series data. The proposed model is different from previous ones as the learning focus is shifted from the short-term pattern differences across users to the pattern difference over time for an individual user. The proposed model accommodates well for the highly dynamic nature of the inputs and maximally removes individual characteristics from the prediction result. To analyze the sparse time-series speech, a new data sampling technique will be used to address the imbalanced data problem, and a data quality metric will be developed for the proposed model. 3. The team will conduct an 18-month in-lab evaluation and a 28-month in-home evaluation with a focus on whether the VAS tasks and features from the in-lab evaluation and the repetition features of the in-home VAS data can measure and predict cognitive decline in the in-home participants over time. The proposed methods will be integrated into an interactive system to enable efficient communication on cognitive decline among patients, caregivers, and clinicians. If successful, the outcomes of this project will provide an opportunity to provide supportive evidence to clinicians for the early detection of cognitive impairment outside of a clinic-based setting.

早期发现阿尔茨海默氏病和相关痴呆症（ADRD）涉及的认知下降 在老年人中，独自生活对于制定，计划和启动干预和支持至关重要 改善患者日常功能和生活质量的系统。常规的基于诊所的方法 早期诊断很昂贵，耗时，并且不切实际地进行大规模筛查。这个项目 旨在使用语音助手开发低成本，被动和实用的家庭评估方法 用于早期检测认知下降的系统（VAS），包括一套新的数据挖掘技术 稀疏的时间序列演讲。该项目具有三个特定的目的：1。使用经常性神经网络 （RNN）和SoftMax回归模型，我们将开发一种转移学习技术来研究 从LAB VAS任务中的语音与认知能力下降之间的联系。皮特语料库 Dementiabank数据库将用于优化RNN参数，从而克服有限 VAS的数据问题。 SoftMax回归模型将使我们能够从 先前的语音数据和LAB内VAS语音。 2。我们将发展一个小说的“多差异”预测 具有对称RNN结构的模型，可以预测一段时间的两端的认知差异 稀疏的时间序列数据。提出的模型与以前的模型不同，因为学习重点是 从用户之间的短期模式差异转变为随着时间的流逝的模式差异 个人用户。提出的模型很好地适应了投入的高度动态性质和 最大程度地从预测结果中删除个人特征。分析稀疏的时间序列 语音，一种新的数据抽样技术将用于解决不平衡的数据问题以及数据 将为建议的模型开发质量指标。 3。团队将进行18个月的内行 评估和28个月的室内评估，重点是VAS任务和功能。 单行内评估和内部VAS数据的重复特征可以测量和预测 随着时间的推移，家庭参与者的认知能力下降。提出的方法将集成到 互动系统可以使患者，看护人和 临床医生。如果成功，该项目的结果将为提供支持性提供机会 向临床医生向基于诊所的环境以外的认知障碍的早期发现的证据。