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.

在独自生活的老年人中，早期发现阿尔茨海默氏病和相关痴呆症（ADRD）对于开发，计划和启动干预措施和支持系统至关重要，以改善患者的每日功能和生活质量。常规的基于诊所的早期诊断方法是昂贵，不切实际且耗时的。该项目旨在使用语音助理系统（VAS）来开发一种低成本，被动和实用的家庭评估方法，以早日检测ADRD，其中包括一系列新型数据挖掘技术，用于稀疏时序列语音。该项目具有三个特定的目的：1。使用复发性神经网络（RNN）和软磁回归模型，我们将开发一种转移学习技术，以调查来自LAB内VAS In-LAB VAS任务与认知下降的语音之间的联系，并发现与ADRD相关的语音生物标志物。 Pitt语料库语音数据库将用于优化RNN参数，从而克服有限的VAS数据问题。 SoftMax回归模型将使我们能够根据先前的语音数据和LAB VAS语音对齐特征分布； 2。我们将开发一种具有对称RNN结构的新颖的“多差异”预测模型，以预测一段时间内从稀疏的时间序列数据中的两个末端的ADRD相关的认知差异。提出的模型与以前的模型不同，因为学习重点从用户之间的短期模式差异转变为单个用户随时间的模式差异。提出的模型可容纳投入的高度动态性质和最大程度的方法从预测结果中消除了个体特征。为了分析稀疏的时间序列语音，将使用一种新的数据采样技术来解决不平衡的数据问题，并将为提出的模型开发数据质量指标。 3。该团队将进行18个月的单击评估，并进行28个月的室内评估，重点关注LAB评估的VAS任务和功能以及内部VAS数据的重复特征是否可以衡量并预测ADRD与ADRD相关的认知能力下降。提出的方法将集成到交互式系统中，以使患者，看护人和临床医生之间的ADRD状况有效地沟通。成功，该项目的结果将为临床医生提供支持证据，以便在基于诊所的环境之外早期发现ADRD。 项目相关性 该项目旨在使用语音助理系统（VAS）来开发低成本，被动和实用的认知评估方法，以早日检测与ADRD相关的认知能力下降。如果成功的话，建议的系统可能会因早期诊断方式而被广泛传播，这些模式最终可以使长期的患者和护理人员计划在家中保持个人的独立性。