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

在独居老年人中早期发现阿尔茨海默病和相关痴呆（ADRD）对于制定、规划和启动干预和支持系统以改善患者的日常功能和生活质量至关重要。传统的、基于临床的早期诊断方法昂贵、不切实际且耗时。本项目旨在利用语音助理系统（VAS）开发一种低成本、被动、实用的家庭评估方法，用于ADRD的早期检测，包括一套新的稀疏时间序列语音数据挖掘技术。该项目有三个具体目标：1。使用循环神经网络（RNN）和softmax回归模型，我们将开发一种迁移学习技术来研究实验室VAS任务中的语音与认知能力下降之间的联系，并发现与adrd相关的语音生物标志物。将使用Pitt语料库语音数据库来优化RNN参数，从而克服VAS数据有限的问题。softmax回归模型将允许我们对齐先前语音数据和实验室VAS语音的特征分布；2. 我们将开发一种具有对称RNN结构的新型“多对差”预测模型，从稀疏时间序列数据中预测时间段两端与adrd相关的认知差异。所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移到单个用户随着时间的模式差异。所提出的模型很好地适应了输入的高度动态性，并最大限度地从预测结果中去除个体特征。为了分析稀疏时间序列语音，将使用一种新的数据采样技术来解决数据不平衡问题，并为所提出的模型开发数据质量度量；3. 该团队将进行为期18个月的实验室评估和28个月的家庭评估，重点关注实验室评估的VAS任务和特征以及家庭VAS数据的重复特征是否可以测量和预测家庭参与者随着时间的推移与adrd相关的认知衰退。建议的方法将集成到一个互动系统中，以便在患者，护理人员和临床医生之间有效地沟通ADRD状态。如果成功，该项目的结果将提供一个机会，为临床医生在临床环境之外早期发现ADRD提供支持性证据。

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.