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

> **NIH NIH R01** · UNIVERSITY OF MASSACHUSETTS BOSTON · 2020 · $296,138

## Abstract

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.

## Key facts

- **NIH application ID:** 10019452
- **Project number:** 5R01AG067416-02
- **Recipient organization:** UNIVERSITY OF MASSACHUSETTS BOSTON
- **Principal Investigator:** Xiaohui Liang
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $296,138
- **Award type:** 5
- **Project period:** 2019-09-30 → 2023-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10019452

## Citation

> US National Institutes of Health, RePORTER application 10019452, SCH: INT: Collaborative Research: Exploiting Voice Assistant Systems for Early Detection of Cognitive Decline (5R01AG067416-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10019452. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*