# A clinician-in-the-loop smart home to support health monitoring and intervention for chronic conditions

> **NIH NIH R01** · WASHINGTON STATE UNIVERSITY · 2020 · $354,490

## Abstract

PROJECT SUMMARY/ABSTRACT
 The world's population is aging and the increasing number of older adults with chronic health conditions is
a challenge our society must address. While the idea of smart environments is now a reality, there remain gaps
in our knowledge about how to scale smart homes technologies for use in complex settings and to use machine
learning and activity learning technologies to design automated health assessment and intervention strategies.
The long-term objective of this project is to improve human health and impact health care delivery by
developing smart environments that aid with health monitoring and intervention. The primary objective of this
application is to design a “clinician in the loop” smart home to empower individuals in managing their chronic
health conditions by automating health monitoring, assessment, and evaluation of intervention impact.
Building on our prior work, the approach will be to generate analytics describing an individual's behavior
routine using smart homes, smart phones, and activity learning (Aim 1). Our trained clinicians will use the
analytics to perform health assessment and detection of health events (Aim 2). In addition, we will introduce
brain health interventions to support sustainable improvement of brain health (Aim 3). Finally, we train
machine learning algorithms from the clinical observations to automate assessment of health and intervention
impact (Aim 4). The use of these technologies is expected to improve and extend the functional health and
wellbeing of older adults, lead to more proactive and preventative health care, and reduce the caregiver burden
of health monitoring and assistance. By understanding situational factors that impact prompt adherence,
adherence situations can be increased. The approach is innovative because it will explore and validate new
machine learning techniques for activity learning and health assessment based on clinical ground truth. These
contributions are significant because they can extend the health self-management of our aging society through
proactive health care and real-time intervention, and reduce the emotional and financial burden for caregivers
and society. Given nursing home care costs, the impact of family-based care, and the importance that people
place on staying at home, technologies that increase functional independence and thus support aging in place
while improving quality of life for both individuals and their caregivers are of significant value to both
individuals and society.

## Key facts

- **NIH application ID:** 9934286
- **Project number:** 5R01NR016732-04
- **Recipient organization:** WASHINGTON STATE UNIVERSITY
- **Principal Investigator:** Diane Joyce Cook
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $354,490
- **Award type:** 5
- **Project period:** 2017-08-01 → 2022-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9934286

## Citation

> US National Institutes of Health, RePORTER application 9934286, A clinician-in-the-loop smart home to support health monitoring and intervention for chronic conditions (5R01NR016732-04). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9934286. Licensed CC0.

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