Abstract
TigerPlace is presented as an example of senior housing which has incorporated smart home technology into senior apartments. The goal of TigerPlace was to develop a true aging in place housing site in which residents move into independent apartments and stay through the end of life. In-home sensing technology is used to augment care coordination to detect early signs of illness and functional decline to allow early interventions aimed at proactively keeping residents healthy. Over 55 apartments have been outfitted with in-home sensor networks since 2005 with an average longevity of 2.6 years. Sensors tested in TigerPlace apartments include motion sensors, bed sensors, stove temperature sensors, and gait/fall detection sensors. All sensors are environmentally mounted and designed to be as invisible as possible. Studies have investigated which sensors and sensor features are most important for detecting early illness signs. A prospective research study at TigerPlace using in-home sensing and automated health alerts showed significant differences in health outcomes between a control group receiving normal care and an intervention group with sensors and automated health alerts.
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Anderson D, Luke RH, Keller JM, Skubic M, Rantz M, Aud M (2009) Linguistic summarization of video for fall detection using voxel person and fuzzy logic. Comput Vis Image Underst 113(1):80–89
Demiris G, Hensel BK, Skubic M, Rantz MJ (2008) Senior residents’ perceived need of and preferences for smart home sensor technologies. Int J Technol Asses Health Care 24(1):102–124
Demiris G, Parker-Oliver D, Giger J, Skubic M, Rantz M (2009) Older adults’ privacy considerations for vision based recognition methods of eldercare applications. Technol Health Care 17(1):41–48
Galambos C, Skubic M, Wang S, Rantz M (2013) Management of dementia and depression utilizing inhome passive sensor data. Gerontechnol J 11(3):457–468
Heise D, Rosales L, Skubic M, Devaney MJ (2011) Refinement and evaluation of a hydraulic bed sensor. In: Proceeding of the IEEE engineering in medicine and biology conference, Boston, 30 Aug–3 Sept 2011
Liu L, Popescu M, Rantz M, Skubic M, Cuddihy P, Yardibi T (2011) Automatic fall detection based on doppler radar motion signature. In: Proceeding of the pervasive health conference, Dublin, May 2011
Mack DC, Patrie JT, Suratt PM, Felder RA, Alwan M (2009) Development and preliminary validation of heart rate and breathing rate detection using a passive, ballistocardiography-based sleep monitoring system. IEEE Trans IT Biomed 13:111–120
Rantz MJ, Skubic M, Alexander G, Popescu M, Aud M, Koopman R, Miller S (2010) Developing a comprehensive electronic health record to enhance nurse care coordination, use of technology, and research. J Gerontol Nurs 36(1):13–17
Rantz MJ, Skubic M, Alexander G, Phillips L, Aud M, Wakefield B, Koopman R, Miller S (2012) Automated technology to speed recognition of signs of illness in older adults. J Gerontol Nurs 38(4):18–23
Rosales L, Skubic M, Heise D, Devaney MJ, Schaumburg M (2012) Heartbeat detection from a hydraulic bed sensor using a clustering approach. In: Proceeding of the IEEE Eng in medicine and biology conference, San Diego, 28 Aug–1 Sept 2012
Skubic M, Alexander G, Popescu M, Rantz M, Keller J (2009) A smart home application to eldercare: current status and lessons learned. Technol Health Care 17(3):183–201
Skubic M, Guevara RD, Rantz M (2012) Testing classifiers for embedded health assessment. In: Proceeding, international conference on smart homes and health telematics, Artimino, pp 198–205
Starr I, Rawson AJ, Schroeder HA, Joseph NR (1939) Studies on the estimation of cardiac output in man, and of abnormalities in cardiac function, from the heart’s recoil and the blood’s impacts; the Ballistocardiogram. Am J Physiol 127(1):1–28
Stone E, Skubic M (2011) Evaluation of an inexpensive depth camera for in-home gait assessment. J Ambient Intell Smart Environ 3(4):349–361
Stone E, Skubic M (2013) Unobtrusive, continuous, in-home gait measurement using the Microsoft Kinect. IEEE Trans Biomed Eng 60(10):2925–2932
Stone E, Skubic M (2014) Fall detection in homes of older adults using the Microsoft Kinect. IEEE J Biomed Health Inform (in press)
Wang S (2011) Change detection for eldercare using passive sensing. PhD dissertation, Electrical and Computer Engineering Department, University of Missouri, Columbia
Wang S, Skubic M, Zhu Y (2012) Activity density map visualization and similarity comparison for eldercare monitoring. IEEE Trans IT Biomed 16(4):607–614
Wang F, Stone E, Skubic M, Keller JM, Abbott C (2013) Towards a passive low-cost in-home gait assessment system for older adults. IEEE J Biomed Health Inform 17(2):346–355
Yardibi T, Cuddihy P, Genc S, Bufi C, Skubic M, Rantz M, Liu L, Phillips II C (2011) Gait characterization via Pulse-Doppler Radar. In: Proceeding pervasive computing, smartE workshop, Seattle, Mar 2011
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© 2014 Springer International Publishing Switzerland
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Skubic, M., Rantz, M.J. (2014). TigerPlace. In: van Hoof, J., Demiris, G., Wouters, E. (eds) Handbook of Smart Homes, Health Care and Well-Being. Springer, Cham. https://doi.org/10.1007/978-3-319-01904-8_37-1
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DOI: https://doi.org/10.1007/978-3-319-01904-8_37-1
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