SBIR-STTR Award

Advances in health care have been dramatic since the beginning of the millennium. As a result, people are living longer with age-related diseases, and the number of older individuals unable to live independently is rising rapidly. Mobile computing offers an unprecedented opportunity to unobtrusively monitor behavior and use the large amount of collected data to automate analysis of functional health in a person's everyday settings. This project focuses on evaluating the performance and commercial viability of technologies that will meet some of the needs that this coming age wave introduces by automating assessment of a person's functional performance. The primary objective of this Phase I STTR application is to evaluate the feasibility of assessing an individual's cognitive and mobility-based health using behavior patterns as sensed by a smart watch. Achieving this objective will provide a foundation for the Phase II application goal of using multiple information sources to automatically-generate activity scores and functional health measures from sensor data. Building on our prior collaborative work, our approach creates a profile of a person's routine behavior through automated real-time recognition of complex activities from mobile sensor data (Aim 1). We will evaluate the use of machine learning techniques to map behavior features onto cognitive and mobility health scores provided through app and in-person neuropsychological assessment (Aim 2). Finally, we will evaluate an interactive visual tool for displaying behavior patterns to provide individuals and their caregivers with insights on their routines and relationship with their health status (Aim 3). This work has important health care implications as functional impairment has been associated with negative outcomes including increased health care utilization, falls, and conversion to dementia. Given nursing home care costs, the impact of family-based care, and the importance that people place on staying at home, it is imperative to commercialize technologies that increase functional independence while improving quality of life for both individuals and their caregivers.

Public Health Relevance Statement:
PROJECT NARRATIVE We propose to evaluate the performance and commercial viability of an application that can predict an individual's cognitive and mobility-based health based on patterns that are sensed by a smart watch, and effectively and efficiently present this information to the caregiver. This work will lay the foundation for a commercial tool to analyze behavior routines and automate assessment of functional health. 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.

Project Terms:
Active Learning; Address; Adult; Age; age related; Aging; Apple; Area; automated analysis; Awareness; base; Behavior; Behavior monitoring; Behavioral; Businesses; care costs; Caregiver Burden; Caregivers; Caring; Chronic; Clinical; Cognition; Cognitive; Collaborations; Commercial Sectors; commercialization; commercially viable technology; Complex; Data; Data Sources; Dementia; design; Disease; Elderly; Environment; Evaluation; experience; falls; Family; Family Planning; family support; Feasibility Studies; Feedback; Foundations; functional disability; functional independence; Goals; Health; health assessment; health care service utilization; Health Status; Healthcare; Home environment; Home Nursing Care; Impaired cognition; improved; Independent Living; Individual; innovation; insight; Interdisciplinary Study; Intervention; iterative design; Label; learning strategy; Light; Machine Learning; Maps; Measures; Methods; mobile application; mobile computing; Modeling; Neuropsychology; Outcome; Participant; Pattern; Performance; personalized care; Persons; Phase; Population; Process; prototype; Public Health; Quality of life; recruit; Research; research clinical testing; Research Personnel; research study; Role; Self Management; sensor; Small Business Technology Transfer Research; smart watch; Societies; Source; supervised learning; Techniques; Technology; Testing; Time; tool; treatment planning; Visual; Work

The world's population is aging and the increasing number of older adults with Alzheimer's disease andrelated dementias (ADRDs) is a challenge our society must address. While the future of healthcare availabilityand quality of services seems uncertain, at the same time advances in pervasive computing and intelligentembedded systems provide innovative strategies to meet these needs. Two particular needs which technologycan help address is early detection of cognitive and physical decline, and tracking integration of new, healthybrain behaviors into everyday life. The long-term goal for Adaptelligence LLC is to commercialize a smartwatchapp, called AcTelligence, to assess a person's cognitive and physical health and to promote healthy brainbehavior. The objective of this application is to perform research a development to refine and commercialize asmartwatch app that offers capabilities to detect activities of daily living from smartwatch sensors, extractdigital behavior markers from activity-labeled sensor data, predict clinical health measures from behaviormarkers, and provide user feedback in the form of health status and healthy-behavior prompts. This technologyis unique because we consider a person's entire behavior profile and introduce machine learning methods torobustly predict clinical measures from this information. We utilize a popular smartwatch platform to increaseaccessibility and balance continuous assessment with opportunities to extend and improve health. Building onour successful Phase I effort, our approach is to extract activity-aware digital behavior markers fromsmartwatch sensor data (Aim 1), automate health assessment based on these markers (Aim 2), and performparticipatory design of a web dashboard that provides visual analytics and alerts for brain health (Aim 3). Wewill validate the sensing and machine learning technologies for a sample of 100 older adults and will refine theinteractive analytics through multiple rounds of participatory design with 18 participants. The app will bebrought to market through a thorough market analysis and a strategically-designed commercialization plan.The proposed contributions are significant because they will provide insights on cognitive and physical healthrevealed within a person's everyday environment that promote early detection of cognitive and physical declinethat can lead to more effective treatment. This work is important because of the increasing number of olderindividuals experiencing cognitive and functional limitations due to chronic health conditions. Furthermore,the work addresses the need for individuals to remain functionally independent as long as possible in their ownhomes, thereby improving quality of life and reducing health care costs.

Public Health Relevance Statement:
PROJECT NARRATIVE We propose to perform research and development to refine and extend a commercial application that can predict an individual's cognitive and mobility-based health using patterns learned from sensor data collected from a smart watch, and effectively and efficiently present this information to the caregiver. The application also provides opportunities to learn about healthy brain behavior and to track the integration of these behaviors into a person's routine. 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.

Project Terms:
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