Abstract
The development of sensor technologies in smart homes helps to increase user comfort or to create safety through the recognition of emergency situations. For example, lighting in the home can be controlled or an emergency call can be triggered if sensors hidden in the floor detect a fall of a person. It makes sense to also use these technologies regarding prevention and early detection of diseases. By detecting deviations and behavioral changes through long-term monitoring of daily life activities it is possible to identify physical or cognitive diseases. In this work, we first examine in detail the existing possibilities to recognize the activities of daily life and the capability of such a system to conclude from the given data on illnesses. Then we propose a model for the use of floor-based sensor technology to help diagnose diseases and behavioral changes by analyzing the time spent in bed as well as the walking speed of users. Finally, we show that the system can be used in a real environment.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Addlesee, M.D., Jones, A., Livesey, F., Samaria, F.: The orl active floor [sensor system]. IEEE Pers. Commun. 4(5), 35–41 (1997). https://doi.org/10.1109/98.626980
Aicha, A.N., Englebienne, G., Kröse, B.: Continuous measuring of the indoor walking speed of older adults living alone. J. Ambient. Intell. Humanized Comput. 9(3), 589–599 (2018). https://doi.org/10.1007/s12652-017-0456-x
Asplund, R.: Mortality in the elderly in relation to nocturnal micturition. BJU International 84(3), 297–301 (1999)
Barker, J.C., Mitteness, L.S.: Nocturia in the elderly. Gerontologist 28(1), 99–104 (1988)
Braun, A., Heggen, H., Wichert, R.: CapFloor – a flexible capacitive indoor localization system. In: Chessa, S., Knauth, S. (eds.) EvAAL 2011. CCIS, vol. 309, pp. 26–35. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33533-4_3
Coyne, K., Zhou, Z., Bhattacharyya, S., Thompson, C., Dhawan, R., Versi, E.: The prevalence of nocturia and its effect on health-related quality of life and sleep in a community sample in the usa. BJU International 92(9), 948–954 (2003)
Dai, J., Bai, X., Yang, Z., Shen, Z., Xuan, D.: PerFallD: a pervasive fall detection system using mobile phones. In: 2010 8th IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops), pp. 292–297, March 2010. https://doi.org/10.1109/PERCOMW.2010.5470652
Fleury, A., Vacher, M., Noury, N.: SVM-based multimodal classification of activities of daily living in health smart homes: sensors, algorithms, and first experimental results. IEEE Trans. Inf. Technol. Biomed. 14(2), 274–283 (2010). https://doi.org/10.1109/TITB.2009.2037317
Fu, B., Kirchbuchner, F., von Wilmsdorff, J., Grosse-Puppendahl, T., Braun, A., Kuijper, A.: Indoor localization based on passive electric field sensing. In: Braun, A., Wichert, R., Maña, A. (eds.) Ambient Intelligence, pp. 64–79. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-56997-0_5
Gjoreski, M., Gjoreski, H., Lutrek, M., Gams, M.: How accurately can your wrist device recognize daily activities and detect falls? Sensors 16(6) (2016). https://doi.org/10.3390/s16060800, http://www.mdpi.com/1424-8220/16/6/800
Hagler, S., Austin, D., Hayes, T.L., Kaye, J., Pavel, M.: Unobtrusive and ubiquitous in-home monitoring: A methodology for continuous assessment of gait velocity in elders. IEEE Trans. Biomed. Eng. 57(4), 813–820 (2010). https://doi.org/10.1109/TBME.2009.2036732
Haustein, T., Mischke, J., Schönfeeld, F., Willand, I.: Ältere Menschen in Deutschland und der EU. Statistisches Bundesamt (2016)
He, Y., Li, Y., Bao, S.D.: Fall detection by built-in tri-accelerometer of smartphone. In: Proceedings of 2012 IEEE-EMBS International Conference on Biomedical and Health Informatics. pp. 184–187, January 2012. https://doi.org/10.1109/BHI.2012.6211540
Hublin, C., Partinen, M., Koskenvuo, M., Kaprio, J.: Sleep and mortality: a population-based 22-year follow-up study. Sleep 30(10), 1245–1253 (2007)
Kearns, W.D., et al.: Path tortuosity in everyday movements of elderly persons increases fall prediction beyond knowledge of fall history, medication use, and standardized gait and balance assessments. J. Am. Med. Dir. Assoc. 13(7), 665–e7 (2012)
Kearns, W.D., Nams, V., Fozard, J.L.: Tortuosity in movement paths is related to cognitive impairment. Methods Inf. Med. 49(06), 592–598 (2010)
van Kerrebroeck, P., Abrams, P., Chaikin, D., Donovan, J., Fonda, D., Jackson, S., Jennum, P., Johnson, T., Lose, G., Mattiasson, A.: The standardisation of terminology in Nocturia: report from the standardisation sub-committee of the international continence society. Neurourol. Urodyn. Off. J. Int. Cont. Soc. 21(2), 179–183 (2002)
Kirchbuchner, F., Grosse-Puppendahl, T., Hastall, M.R., Distler, M., Kuijper, A.: Ambient intelligence from senior citizens’ perspectives: understanding privacy concerns, technology acceptance, and expectations. In: De Ruyter, B., Kameas, A., Chatzimisios, P., Mavrommati, I. (eds.) AmI 2015. LNCS, vol. 9425, pp. 48–59. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-26005-1_4
Lauterbach, C., Steinhage, A.: Sensfloor®-a large-area sensor system based on printed textiles printed electronics. In: Ambient Assisted Living Congress. VDE Verlag (2009)
Lightner, D.J., et al.: Nocturia is associated with an increased risk of coronary heart disease and death. BJU international 110(6), 848–853 (2012)
McDaniel, S., Zimmer, Z.: Global ageing in the twenty-first century: challenges, opportunities and implications. Ashgate Publishing Ltd (2013)
Miaou, S.G., Sung, P.H., Huang, C.Y.: A customized human fall detection system using omni-camera images and personal information. In: 1st Transdisciplinary Conference on Distributed Diagnosis and Home Healthcare, D2H2, pp. 39–42, April 2006. https://doi.org/10.1109/DDHH.2006.1624792
Middelkoop, H.A., Smilde-van den Doel, D.A., Neven, A.K., Kamphuisen, H.A., Springer, C.P.: Subjective sleep characteristics of 1,485 males and females aged 50–93: effects of sex and age, and factors related to self-evaluated quality of sleep. J. Gerontol. Ser. A Biol. Sci. Med. Sci. 51(3), M108–M115 (1996)
Montero-Odasso, M., et al.: Gait velocity as a single predictor of adverse events in healthy seniors aged 75 years and older. J. Gerontol. Ser. A Biol. Sci. Med. Sci. 60(10), 1304–1309 (2005)
Pantelopoulos, A., Bourbakis, N.G.: A survey on wearable sensor-based systems for health monitoring and prognosis. IEEE Trans. Syst., Man, Cybern., Part C (Appl. Rev.) 40(1), 1–12 (2010). https://doi.org/10.1109/TSMCC.2009.2032660
Quach, L., et al.: The nonlinear relationship between gait speed and falls: the maintenance of balance, independent living, intellect, and zest in the elderly of Boston study. J. Am. Geriatr. Soc. 59(6), 1069–1073 (2011)
Rantz, M.J., et al.: A new paradigm of technology-enabled vital signs’ for early detection of health change for older adults. Gerontology 61(3), 281–290 (2015)
Rashidi, P., Mihailidis, A.: A survey on ambient-assisted living tools for older adults. IEEE J. Biomed. Health Inform. 17(3), 579–590 (2013). https://doi.org/10.1109/JBHI.2012.2234129
Ribeiro Filho, J.D.P., e Silva, F.J.d.S., Coutinho, L.R., Gomes, B.d.T.P.: MHARS: a mobile system for human activity recognition and inference of health situations in ambient assisted living. J. Appl. Comput. Res. 5(1), 44–58 (2016)
Sheridan, P.L., Hausdorff, J.M.: The role of higher-level cognitive function in gait: executive dysfunction contributes to fall risk in Alzheimers Disease. Dement. Geriatr. Cogn. Disord. 24(2), 125–137 (2007)
Skubic, M., Guevara, R.D., Rantz, M.: Automated health alerts using in-home sensor data for embedded health assessment. IEEE J. Transl. Eng. Health Med. 3, 1–11 (2015). https://doi.org/10.1109/JTEHM.2015.2421499
Stenholm, S., Kronholm, E., Bandinelli, S., Guralnik, J.M., Ferrucci, L.: Self-reported sleep duration and time in bed as predictors of physical function decline: results from the inchianti study. Sleep 34(11), 1583–1593 (2011). https://doi.org/10.5665/sleep.1402
Vacher, M., Fleury, A., Portet, F., Serignat, J.F., Noury, N.: Complete sound and speech recognition system for health smart homes: application to the recognition of activities of daily living (2010)
Verghese, J., Wang, C., Holtzer, R., Lipton, R., Xue, X.: Quantitative gait dysfunction and risk of cognitive decline and dementia. J. Neurol., Neurosurg. Psychiatry (2007)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Scherf, L., Kirchbuchner, F., von Wilmsdorff, J., Fu, B., Braun, A., Kuijper, A. (2018). Step by Step: Early Detection of Diseases Using an Intelligent Floor. In: Kameas, A., Stathis, K. (eds) Ambient Intelligence. AmI 2018. Lecture Notes in Computer Science(), vol 11249. Springer, Cham. https://doi.org/10.1007/978-3-030-03062-9_11
Download citation
DOI: https://doi.org/10.1007/978-3-030-03062-9_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-03061-2
Online ISBN: 978-3-030-03062-9
eBook Packages: Computer ScienceComputer Science (R0)