This proposal is for an NLM sponsored K01 Mentored Career Development Award. The research I propose involves the development of a smart mobile application combined with commercially available sensors, to capture actionable information about behavior pattern changes in both patients with dementia and their primary caregivers. Through continuously collected physiological sensor data, I will build data models that can learn the behavior patterns of both the patient with dementia and his/her caregiver. My research plan involves three stages. The first stage involves instrument validation and generating context-specific ground truth or the baseline for each participating couple. The second stage will involve longitudinal observational study using the system built in the first stage in an assisted living facility. The final stage will involve deploying our system for a longitudinal study in the homes of our participants. This study merges well with my short term goals to learn more about the challenges faced by older adults as they battle physical and cognitive decline. As a computer science researcher, my long term goal is to work on building technologies that allow older couples to live independently in their homes for a longer time, and to have more control over their quality of life. I have experience working in eldercare research as a part of my doctoral studies in University of Missouri's Center for Eldercare and Rehabilitation Technology. I plan to continue working in this field and develop my independent research program that facilitates aging in place through the use of low cost sensors and machine learning analytics. Currently as an Instructor and Research Assistant Professor at Wright State University's Department of Computer Science and Engineering, I have strong mentors both in the technical and clinical areas that will allow me to grow in this highly interdisciplinary area. My career development plan includes coursework, regular meetings with mentors and collaborators, and practical clinical study experience that will facilitate my development of an independent research program that will enrich and improve the lives of older adults for years to come.
With the spread of smartphones and wearable sensors in the fitness industry, there is a great opportunity for personalized digital health, especially for chronic conditions like dementia. As an increasing cohort of older adults suffer from dementia, there arises a strong need to extract relevant patient symptoms collected using sensors to provide actionable information for timely intervention. The objective of this application is to map data collected from physiological sensors with the behavior patterns of persons with dementia, as well as the caregiver to understand the physiological changes associated with behavior changes to facilitate early intervention through timely alerts to clinicians.
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