Research Article| Volume 67, ISSUE 2, P140-153, March 2019

A nurse-driven method for developing artificial intelligence in “smart” homes for aging-in-place

Published:November 22, 2018DOI:


      • Nurses make significant contributions to artificial intelligence (AI) development
      • Nurse-driven AI may facilitate aging-in-place with smart homes
      • Nurses need practical methods for working with AI development for healthcare



      To offer practical guidance to nurse investigators interested in multidisciplinary research that includes assisting in the development of artificial intelligence (AI) algorithms for “smart” health management and aging-in-place.


      Ten health-assistive Smart Homes were deployed to chronically ill older adults from 2015 to 2018. Data were collected using five sensor types (infrared motion, contact, light, temperature, and humidity). Nurses used telehealth and home visitation to collect health data and provide ground truth annotation for training intelligent algorithms using raw sensor data containing health events.


      Nurses assisting with the development of health-assistive AI may encounter unique challenges and opportunities. We recommend: (a) using a practical and consistent method for collecting field data, (b) using nurse-driven measures for data analytics, (c) multidisciplinary communication occur on an engineering-preferred platform.


      Practical frameworks to guide nurse investigators integrating clinical data with sensor data for training machine learning algorithms may build capacity for nurses to make significant contributions to developing AI for health-assistive Smart Homes.


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        • Bhaskar R.
        A realist theory of science.
        2nd ed. Harvester, Brighton, UK1978
        • Bhaskar R.
        Reclaiming reality: A critical introduction to contemporary philosophy.
        Verso, London1989
        • Bhatta B.
        Research methods in remote sensing.
        Springer, London2013
        • Bian Z.
        • Hou J.
        • Chau L.
        • Magnenat-Thalmann N.
        Fall detection based on body part tracking using a depth camera.
        IEEE Journal of Biomedical and Health Informatics. 2015; 19: 430-439
        • Boise L.
        • Wild K.
        • Mattek N.
        • Ruhl M.
        • Dodge H.
        • Kaye J.
        • et al.
        Willingness of older adults to share data and privacy concerns after exposure to unobtrusive home monitoring.
        Gerontechnology: International Journal on the Fundamental Aspects of Technology to Serve the Ageing Society. 2013; 11: 428-435
        • Brynjolfsson E.
        • Mitchell T.
        • et al.
        What can machine learning do? Workforce implications.
        Science. 2017; 358: 1530-1534
        • Campolo A.
        • Sanfilippo M.
        • Whittaker M.
        • Crawford K.
        AI now 2017 report.
        New York University, 2017
        • Cook D.J.
        • Crandall A.
        • Thomas B.
        • Krishnan N.
        CASAS: A smart home in a box.
        IEEE Comput. 2012; 46: 62-69
        • Cook D.
        • Das S.
        Smart environments technology, protocols and applications, Vol. 99 (pp. 1--1).
        John Wiley, Hoboken2004
        • Cook D.J.
        • Schmitter-Edgecombe M.
        • Jonsson L.
        • Morant A.V.
        Technology-enabled assessment of functional health.
        IEEE Reviews in Biomedical Engineering. 2018; (to appear)
        • Courtney K.L.
        Privacy and senior willingness to adopt smart home information technology in residential care facilities.
        Methods Inf. Med. 2008; 47: 76-81
        • Courtney K.L.
        • Alexander G.L.
        • Demiris G.
        Information technology from novice to expert: Implementation implications.
        Journal of Nursing Management. 2008; 16: 692-699
        • Dermody G.
        • Fritz R.
        A conceptual framework for clinicians working with artificial intelligence and health-assistive smart homes.
        Nursing Inquiry. 2018; : e12267
        • Demiris G.
        Privacy and social implications of distinct sensing approaches to implementing smart homes for older adults.
        in: 31st Annual International Conference of the IEEE EMBS, Minneapolis, Minnesota, USA2009: 4311-4314
        • Demiris G.
        • Rantz M.
        • Aud M.
        • Marek K.
        • Tyrer H.
        • Skubic M.
        • Hussam A.
        • et al.
        Older adults’ attitudes towards and perceptions of ‘smart home’ technologies: A pilot study.
        Med. Informatics Internet Med. 2004; 29: 87-94
        • Demiris G.
        • Hensel B.K.
        • Skubic M.
        • Rantz M.
        Senior residents’ perceived need of and preferences for “smart home” sensor technologies.
        International Journal of Technology Assessment in Health Care. 2008; 24: 120-124
        • Demiris G.
        • Rantz M.
        • Aud M.
        • Marek K.
        • Tyrer H.
        • et al.
        Older adults’ attitudes towards and perceptions of “smart home” technologies: A pilot study.
        Medical Informatics and the Internet in Medicine. 2004; 29: 87-94
        • Fetter M.S.
        Improving information technology competencies: Implications for psychiatric mental health nursing.
        Issues in Mental Health Nursing. 2009; 30: 3-13
        • Fritz R.L.
        • Cook D.
        Identifying varying health states in smart home sensor data: An expert-guided approach.
        in: World Multi-Conference of Systemics, Cybernetics and Informatics: WMSCI 2017. Conference paper. Indexed Scopus. 2017
        • Fritz R.L.
        • Corbett C.L.
        • Vandermause R.
        • Cook D.
        The influence of culture on older adults’ adoption of smart home monitoring.
        Gerontechnology. 2016; 14: 146-156
        • Ghods A.
        • Caffrey K.
        • Lin Beiyu
        • Fraga K.
        • Fritz R.
        • Schmitter-Edgecombe M.
        • et al.
        Iterative design of visual analytics for a clinician-in-the-loop smart Home.
        IEEE Journal of Biomedical and Health Informatics. 2018;
        • Idhe D.
        Technology and the lifeworld: From garden to earth.
        Indiana University Press, Indiannapolis, IN1990
      1. Jideofor, V., Young, C., Zaruba, G., & Daniel, K. M. (2012). Intelligent sensor floor for fall prediction and gait analysis. Available on Google Scholar and Semantic Scholar.

        • Kaye J.
        • Maxwell S.
        • Mattek N.
        • Hayes T.L.
        • Dodge H.
        • Pavel M.
        • et al.
        Intelligent systems for assessing aging changes: Home-based, unobtrusive, and continuous assessment of aging.
        The Journals of Gerontology. Series B, Psychological Sciences and Social Sciences. 2011; 66 (suppl 1i180–90)
        • Krishnan N.
        • Cook D.J.
        Activity recognition on streaming sensor data.
        Pervasive Mob. Comput. 2014; 10: 138-154
        • Lê Q.
        • Nguyen H.B.
        • Barnett T.
        Smart homes for older people: Positive aging in a digital world.
        Future Internet. 2012; 4: 607-617
        • Liao P.-H.
        • Hsu P.-T.
        • Chu W.
        • Chu W.-C.
        Applying artificial intelligence technology to support decision-making in nursing: A case study in Taiwan.
        Health Informatics Journal. 2015; 21: 137-148
        • Mann W.
        • Belchior P.
        • Tomita M.
        • Kemp B.
        Older adults’ perception and use of PDAs, home automation system, and home health monitoring system.
        Topics in Geriatric Rehabilitation. 2007; 23: 35-46
      2. McCarthy, J. (n.d.). Programs with Common Sense" at the Wayback Machine. In Proceedings of the Teddington Conference on the Mechanization of Thought Processes (pp. 756–791). London: Her Majesty's Stationery Office.

        • Moen A.
        A nursing perspective to design and implementation of electronic patient record systems.
        Journal of Biomedical Informatics. 2003; 36: 375-378
        • Muheidat F.
        • Tyrer H.W.
        • Popescu M.
        • Rantz M.
        Estimating walking speed, stride length, and stride time using a passive floor based electronic scavenging system.
        In Sensors Applications Symposium (SAS), IEEE. 2017: 1-5
        • National Institute on Aging
        NIH initiative tests in-home technology to help older adults age in place.
        • National Institute on Aging
        Aging in place: Growing old at home.
        • Pearl J.
        • Glymour Madelyn
        • Jewell N.P.
        Causal inference in statistics: A primer.
        John Wiley & Sons, Chichester, West Sussex, UK2016
        • Pierce C.S.
        What is pragmatism.
        The Monist. 1905; 15: 161-181
        • Rantz M.
        • Lane K.
        • Phillips L.J.
        • Despins L.A.
        • Galambos C.
        • Alexander G.L.
        • et al.
        Enhanced registered nurse care coordination with sensor technology: Impact on length of stay and cost in aging in place housing.
        Nursing Outlook. 2015; 63: 650-655
        • Rantz M.
        • Popejoy L.L.
        • Galambos C.
        • Phillips L.J.
        • Lane K.R.
        • Marek K.D.
        • et al.
        The continued success of registered nurse care coordination in a state evaluation of aging in place in senior housing.
        Nursing Outlook. 2014; 62: 237-246
        • Risling T.
        Educating the nurses of 2025: Technology trends of the next decade.
        Nurse Education in Practice. 2017; 22: 89-92
        • Risling T.
        Why AI needs Nursing.
        • Seelye A.M.
        • Schmitter-Edgecombe M.
        • Das B.
        • Cook D.J.
        Application of cognitive rehabilitation theory to the development of smart prompting technologies.
        IEEE Reviews in Biomedical Engineering. 2012; 5: 29-44
        • Skubic M.
        • Guevara R.D.
        • Rantz M.
        Automated health alerts using in-home sensor data for embedded health assessment.
        IEEE Journal of Translational Engineering in Health and Medicine. 2015;
        • Sprint G.
        • Cook D.J.
        • Fritz R.
        • Schmitter-Edgecombe M.
        Using Smart Homes to Detect and Analyze Health Events.
        Computer. 2016; 49: 29-37
        • Sprint G.
        • Cook D.
        • Fritz R.
        • Schmitter-Edgecombe M.
        Detecting health and behavior change by analyzing smart home sensor data.
        in: 2016 IEEE International Conference on Smart Computing (SMARTCOMP). Conference Paper. 2016
        • Steggell C.D.
        • Hooker K.
        • Bowman S.
        • Choun S.
        • Kim S.
        The role of technology for healthy aging among korean and hispanic women in the United States: A pilot study.
        Gerontechnology. 2010; 9: 433-449
        • Stone E.
        • Skubic M.
        Fall detection in homes of older adults using the Microsoft Kinect.
        IEEE J Biomed Health Inform. 2015; 19: 290-301
        • Toromanovic S.
        • Hasanovic E.
        • Masic I.
        Nursing information systems.
        Materia Socio Medica. 2010; 22: 168
        • University of Washington
        Creating safer, smarter homes for seniors.
        • U.S. Census Bureau
        Older people projected to outnumber children for first time in U.S. history.
        • Wagner F.
        • Basran J.
        • Dal Bello-Haas V.
        A review of monitoring technology for use with older adults.
        Journal of Geriatric Physical Therapy. 2012; 35: 28-34
        • Wagner F.
        • Basran J.
        • Dal Bello-Haas V.
        • Maxwell J.a
        • Vurgun S.
        • Vurgun S.
        • et al.
        The elderly's independent living in smart homes: A characterization of activities and sensing infrastructure survey to facilitate services development.
        in: Workshop Proceedings of the 8th International Conference on Intelligent Environments. 3. 2015: 214-253