Sage Journals: Discover world-class research

Abstract

Smart homes equipped with ambient wireless sensor networks provide new opportunities to help older adults age-in-place, improve their quality of life and help better manage their health and wellness. In this paper, we present a methodology that estimates occupants’ status as active, sedentary, in-bed, out-of-home and unobservable, their location in the house, and their daily activities related to overall health and wellness. The methodology is used to visualize and examine the daily patterns and activities of older adults living in their own homes and participating in a smart home research project. The proposed location and status estimation algorithm is highly accurate as validated by a mobile app that prompts participants with questions about the estimated time of their daily activities. A case study involving a significant health-related life event is presented where the participant’s account of changes in her patterns and activities through bi-weekly interviews are shown to confirm inferences based on the results of the proposed methodology.

Keywords

Health and wellness age-in-place rule-based algorithm smart homes wireless sensor networks

Get full access to this article

View all access options for this article.

References

Acampora,

D.J.

Cook,

Rashidi and

A.V.

Vasilakos, A survey on ambient intelligence in healthcare, in: Proceedings of the IEEE, Vol. 101, 2013, pp. 2470–2494.

Alberdi,

Weakley,

Schmitter-Edgecombe and

D.J.

Cook, Smart home-based prediction of multidomain symptoms related to Alzheimer’s disease, Journal of Biomedical and Health Informatics 22(6) (2018), 1720–1731. doi:10.1109/JBHI.2018.2798062.

A.A.

Aramendi,

Weakley,

A.A.

Goenaga,

Schmitter-Edgecombe and

D.J.

Cook, Automatic assessment of functional health decline in older adults based on smart home data, Journal of biomedical informatics 81 (2018), 119–130. doi:10.1016/j.jbi.2018.03.009.

Arifoglu and

Bouchachia, Activity recognition and abnormal behaviour detection with recurrent neural networks, Procedia Computer Science 110 (2017), 86–93. doi:10.1016/j.procs.2017.06.121.

Banerjee,

Yefimova,

J.M.

Keller,

Skubic,

D.L.

Woods and

Rantz, Exploratory analysis of older adults’ sedentary behavior in the primary living area using kinect depth data, Journal of Ambient Intelligence and Smart Environments 9(2) (2017), 163–179. doi:10.3233/AIS-170428.

D.J.

Cook,

J.C.

Augusto and

V.R.

Jakkula, Ambient intelligence: Technologies, applications, and opportunities, Pervasive and Mobile Computing 5(4) (2009), 227–298. doi:10.1016/j.pmcj.2009.04.001.

D.J.

Cook,

A.S.

Crandall,

B.L.

Thomas and

N.C.

Krishnan, CASAS: A smart home in a box, Computer 46(7) (2013), 62–69. doi:10.1109/MC.2012.328.

D.J.

Cook and

Schmitter-Edgecombe, Assessing the quality of activities in a smart environment, Methods of information in medicine 48(5) (2009), 480–485. doi:10.3414/ME0592.

D.J.

Cook,

Youngblood,

E.O.

Heierman,

Gopalratnam,

Rao,

Litvin and

Khawaja, MavHome: An agent-based smart home, in: Proceedings of 1st IEEE International Conference on Pervasive Computing and Communications, PerCom 2003, 2003, pp. 521–524.

10.

P.N.

Dawadi,

D.J.

Cook and

Schmitter-Edgecombe, Automated cognitive health assessment from smart home-based behavior data, IEEE Journal of Biomedical and Health Informatics 20(4) (2016), 1188–1194. doi:10.1109/JBHI.2015.2445754.

11.

H.H.

Dodge,

N.C.

Mattek,

Austin,

T.L.

Hayes and

J.A.

Kaye, In-home walking speeds and variability trajectories associated with mild cognitive impairment, Neurology 78(24) (2012), 1945–1952. doi:10.1212/WNL.0b013e318259e1de.

12.

Dodou and

J.C.F.

de Winter, Social desirability is the same in offline, online and paper surveys: A meta-analysis, Computers in Human Behavior 36 (2014), 487–495. doi:10.1016/j.chb.2014.04.005.

13.

Doyle,

Kealy,

Loane,

Walsh,

O’Mullane,

Flynn,

Bond,

Macfarlane,

Bortz and

R.B.

Knapp, An integrated home-based self-management system to support the wellbeing of older adults, Journal of Ambient Intelligence and Smart Environments 6(4) (2014), 359–383. doi:10.3233/AIS-140270.

14.

EI-Khadiri,

Corona,

Rose and

Charpillet, Sleep activity recognition using binary motion sensorsm, in: 2018 IEEE 30th International Conference on Tools with Artificial Intelligence, ICTAI 2018, 2018, pp. 265–269.

15.

Eldercare Workforce Alliance, Caring for an Aging America: Meeting the health care needs of older adults, Washington, DC, US, 2013.

16.

Forum Interagency Forum on Aging-Releases Statistics Older Americans 2016: Key Indicators of Well-Being, Federal Interagency Forum on Aging-Related Statistics, Government Printing Office, Washington, DC, US, 2016.

17.

Foti and

J.S.

Koketsu, Activities of daily living, in: Pedretti’s Occupational Therapy: Practical Skills for Physical Dysfunction, Vol. 7, Elsevier Health Sciences, Amsterdam, Netherlands, 2013, pp. 157–232.

18.

Ghods,

Caffrey,

Lin,

Fraga,

Fritz,

Schmitter-Edgecombe,

Hundhausen and

D.J.

Cook, Iterative design of visual analytics for a clinician-in-the-loop smart home, Journal of Biomedical and Health Informatics 23(4) (2019), 1742–1748. doi:10.1109/JBHI.2018.2864287.

19.

Goonawardene,

Toh and

H.-P.

Tan, Sensor-driven detection of social isolation in community-dwelling elderly, in: International Conference on Human Aspects of IT for the Ages Population, ITAP 2017, Vol. 10298, 2017, pp. 378–392.

20.

Hayes,

Pavel and

Kaye, An approach for deriving continuous health assessment indicators from in-home sensor data, in: Assistive Technology Research Series, Vol. 21, 2008, pp. 130–137.

21.

T.L.

Hayes,

Riley,

Pavel and

J.A.

Kaye, Estimation of rest-activity patterns using motion sensors, in: Proceedings of Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2010, 2010, pp. 2147–2150.

22.

S.S.

Intille,

Larson,

E.M.

Tapia,

J.S.

Beaudin,

Kaushik,

Nawyn and

Rockinson, Using a live-in laboratory for ubiquitous computing research, in: International Conference on Pervasive Computing, Pervasive 2006, 2006, pp. 349–365.

23.

Jain,

Popescu,

Keller,

Ranta and

Markway, Linguistic summarization of in-home sensor data, Journal of Biomedical Informatics 96 (2019), 1–14. doi:10.1016/j.jbi.2019.103240.

24.

J.A.

Kaye,

S.A.

Maxwell,

Mattek,

T.L.

Hayes,

Dodge,

Pavel,

H.B.

Jimison,

Wild,

Boise and

T.A.

Zizelberger, 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 66B (Suppl) 1 (2011), i180–i190.

25.

Kealy,

McDaid,

Loane,

Walsh and

Doyle, Derivation of night time behaviour metrics using ambient sensors, in: 2013 7th International Conference on Pervasive Computing Technologies for Healthcare and Workshop, 2013, pp. 33–40.

26.

C.D.

Kidd,

Orr,

G.D.

Abowd,

C.G.

Atkeson,

I.A.

Essa,

MacIntyre,

Mynatt,

T.E.

Starner and

Newstetter, The aware home: A living laboratory for ubiquitous computing research, in: International Workshop on Cooperative Buildings, CoBuild 1999, 1999, pp. 191–198.

27.

N.C.

Krishnan and

D.J.

Cook, Activity recognition on streaming sensor data, Pervasive and Mobile Computing 10(B) (2014), 138–154. doi:10.1016/j.pmcj.2012.07.003.

28.

M.P.

Lawton and

E.M.

Brody, Assessment of older people: Self-maintaining and instrumental activities of daily living, The Gerontologist 9(3) (1969), 179–186. doi:10.1093/geront/9.3_Part_1.179.

29.

MQTT, http://mqtt.org/.

30.

National Council on Aging The United States of Aging Survey Executive Summary, Arlington, VA, 2015.

31.

O’Brien,

McDaid,

Loane,

Doyle and

O’Mullane, Visualization of movement of older adults within their Homes based on PIR sensor data, in: 2012 6th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops, 2012, pp. 252–259.

32.

Ohta,

Nakamoto,

Shinagawa and

Tanikawa, A health monitoring system for elderly people living alone, Journal of Telemedicine and Telecare 8(3) (2002), 151–156. doi:10.1177/1357633X0200800305.

33.

Palumbo,

La Rosa and

Ferro, Stigmergy-based long-term monitoring of indoor users mobility in ambient assisted living environments: The DOREMI project approach, in: Proceedings of the 2nd Italian Workshop on Artificial Intelligence for Ambient Assisted Living, AI*AAL 2016, Vol. 1803, 2016, pp. 18–32.

34.

Petersen,

Austin,

J.A.

Kaye,

Pavel and

T.L.

Hayes, Unobtrusive in-home detection of time spent out-of-home with applications to loneliness and physical activity, IEEE Journal of Biomedical and Health Informatics 18(5) (2014), 1590–1596. doi:10.1109/JBHI.2013.2294276.

35.

M.J.

Rantz,

K.D.

Marek,

M.A.

Aud,

R.A.

Johnson,

Otto and

Porter, TigerPlace: A new future for older adults, Journal of Nursing Care Quality 20(1) (2005), 1–4. doi:10.1097/00001786-200501000-00001.

36.

Rashidi and

Mihailidis, A survey on ambient-assisted living tools for older adults, Journal of Biomedical and Health Informatics 17(3) (2013), 579–590. doi:10.1109/JBHI.2012.2234129.

37.

S.C.

Reinhard,

L.F.

Feinberg,

Choula and

Houser, Valuing the Invaluable: 2015 Update. Undeniable Progress, but Big Gaps Remain, AARP Public Policy Institute, Washington, DC, US, 2015.

38.

S.C.

Reinhard,

Given,

N.H.

Petlick and

Bemis, Supporting family caregivers in providing care, in: Patient Safety and Quality: An Evidence-Based Handbook for Nurses, Agency for Healthcare Research and Quality,

R.G.

Hughes, ed., Rockville, MD, US, 2008.

39.

Sadri, Ambient intelligence: A survey, ACM Computing Surveys 43(4) (2011), 36:1–36:66. doi:10.1145/1978802.1978815.

40.

Safer, Smarter Homes Start with Z-wave, http://www.z-wave.com/.

41.

Susnea, Engineering human stigmergy, International Journal of Computers Communications & Control 10(3) (2015), 420–427. doi:10.15837/ijccc.2015.3.87.

42.

Susnea,

Dumitriu,

Talmaciu,

Pecheanu and

Munteanu, Unobtrusive monitoring the daily activity routine of elderly people living alone, with low-cost binary sensors, Sensors (Basel) 19(10) (2019).

43.

VandeWeerd,

Yalcin,

Aden-Buie,

Wang,

Roberts,

Mahser,

Fnu and

Fabiano, HomeSense: Design of an Ambient Home Health and Wellness Monitoring Platform for Older Adults, Health and Technology, accepted for publication.

44.

Vaney,

Dixit,

Ghosh,

Gupta and

M.S.

Bhatia, Habituation of event related potentials: A tool for assessment of cognition in headache patients, Delhi Psychiatry Journal 11(1) (2008), 48–51.

45.

Virone,

Alwan,

Dalal,

S.W.

Kell,

Turner,

J.A.

Stankovic and

Felder, Behavioral patterns of older adults in assisted living, IEEE Transactions on Information Technology in Biomedicine 12(3) (2008), 387–398. doi:10.1109/TITB.2007.904157.

46.

Wang,

Skubic and

Zhu, Activity density map visualization and dissimilarity comparison for eldercare monitoring, IEEE Transactions on Information Technology in Biomedicine 16(4) (2012), 607–614. doi:10.1109/TITB.2012.2196439.

Health and wellness monitoring using ambient sensor networks

Abstract

Keywords

Get full access to this article

References