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.

Public Health Relevance

We propose to design and pilot test a ?clinician-in-the-loop? smart home technology to automatically identify health events for adults with chronic conditions in their own homes. The potential health care and social benefits of the proposed work are dramatic as this work will improve the clinical utility, usability and translation of smart home technologies for health assessment and intervention. This work will demonstrate that intelligent technologies can be used in real-world settings to generate analytics which reflect behavior routines and can be used for automated health assessment. This research is relevant to public health because these technologies can extend the functional independence of our aging society through technology-assisted health self-management, reduce caregiver burden and improve quality of life.

National Institute of Health (NIH)
National Institute of Nursing Research (NINR)
Research Project (R01)
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Biomedical Computing and Health Informatics Study Section (BCHI)
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Diana, Augusto
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Washington State University
Engineering (All Types)
Biomed Engr/Col Engr/Engr Sta
United States
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Fritz, Roschelle L; Dermody, Gordana (2018) A nurse-driven method for developing artificial intelligence in ""smart"" homes for aging-in-place. Nurs Outlook :
Cook, Diane J; Duncan, Glen; Sprint, Gina et al. (2018) Using Smart City Technology to Make Healthcare Smarter. Proc IEEE Inst Electr Electron Eng 106:708-722
Dermody, Gordana; Fritz, Roschelle (2018) A conceptual framework for clinicians working with artificial intelligence and health-assistive Smart Homes. Nurs Inq :e12267
Minor, Bryan; Doppa, Janardhan Rao; Cook, Diane J (2017) Learning Activity Predictors from Sensor Data: Algorithms, Evaluation, and Applications. IEEE Trans Knowl Data Eng 29:2744-2757
Williams, Jennifer A; Cook, Diane J (2017) Forecasting behavior in smart homes based on sleep and wake patterns. Technol Health Care 25:89-110
Aminikhanghahi, Samaneh; Cook, Diane J (2017) A Survey of Methods for Time Series Change Point Detection. Knowl Inf Syst 51:339-367