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: 10190783
• 负责人: Xiaohui Liang
• 金额: $ 29.26万
• 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/10190783
• 关键词: Accelerometer; Address; Age; Alzheimer' s Disease; Alzheimer' s disease diagnosis; Alzheimer' s disease related dementia; Biological Markers; Caregivers; Cellular Phone; Cerebrospinal Fluid; Characteristics; Clinic; Clinical; Cognition; Cognitive; Communication; Complement; Complex; Consumption; Custom; Data; Databases; Dementia; Devices; Diagnosis; Diagnostic; Disease; Early Diagnosis; Elderly; Evaluation; Health; Healthcare Systems; Home; Human; Image; Impaired cognition; Individual; Intervention; Knowledge; Lead; Learning; Link; Magnetic Resonance Imaging; Measures; Mental Depression; Methods; Modality; Modeling; Monitor; Nature; Neural Network Simulation; Neuropsychology; Outcome; Participant; Patients; Pattern; Persons; Pharmaceutical Preparations; Pharmacotherapy; Phase; Population; Privacy; Process; Psychological Transfer; Public Health; Quality of life; Research; Research Personnel; Sampling; Science; Series; Services; Speech; Stress; Structure; Support System; System; Tablet Computer; Techniques; Therapeutic Intervention; Time; Visit; Voice; Wrist; actigraphy; aging in place; base; burden of illness; cognitive change; cognitive testing; cost; data mining; data quality; deep learning; handheld mobile device; improved; microphone; novel; predictive modeling; recurrent neural network; screening; sensor; therapeutically effective; touchscreen; usability

Early detection of 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, impractical, and time-consuming. This project aims to develop a low-cost, passive, and practical home-based assessment method using Voice Assistant Systems (VAS) for early detection of ADRD, 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 and discover ADRD-related voice biomarkers. The Pitt Corpus speech 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 ADRD-related cognitive differences 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 ADRD-related cognitive decline in the in-home participants over time. The proposed methods will be integrated into an interactive system to enable efficient communication on ADRD status 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 ADRD outside of a clinic-based setting. Project Relevance This project aims to develop a low-cost, passive, and practical cognitive assessment method using Voice Assistant Systems (VAS) for early detection of ADRD-related cognitive decline. If successful, the proposed system may be widely disseminated for the early diagnosis of ADRD to complement existing diagnostic modalities that could ultimately enable long-term patient and caregiver planning to maintain individual’s independence at home.

早期发现单独生活的老年人和相关的痴呆症（ADRD）对于改善患者的日常功能和生活质量的发展是至关重要的。低成本，被动和实用的家庭评估Mitchod语音助手系统（VAS），用于早期检测ADRD，其中包括一组新的数据挖掘技术，用于稀疏的时间序列演讲。从LAB内的VAS任务和认知发现与ADRD相关的语音生物标志物的转移技术。来自证明TA和LAB内VAS语音的特征分布；我们将开发一个具有对称RNN结构的新颖的预测模型，以预测一段时间的两个端稀疏的时间序列数据与学习的焦点不同是用户与生动用户的模式不同的模式，以供支架模型符合输入的高动态性质，并最大程度地消除了个人特征。从Theze开始，稀疏的时间序列语音解决了不平衡的数据问题，并且将为支撑模型开发数据质量指标；带有焦点任务的评估和外表内的功能以及内部VAS数据的重复特征可以衡量和预测与ADRD相关的认知能力下降，随着时间的流逝，您可以在patibility中进行交流。 ，护理人员和临床医生。 项目相关性 该项目旨在使用与ADRD相关的认知能力下降的语音助手系统（VAS）开发低成本，被动和实用的认知评估方法。汇编的庞大方式可以使长期和照顾者计划在家中保持态度。