Smart Watch and Monitoring System for Dementia Patients

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7861)


Monitoring information on the behavior of dementia patients could improve their health and safety, and thus quality of life. To monitor daily activities, dementia patients require portable and wearable monitoring device. Various sensor technologies are currently used to monitor emergency situations such as falling down and wandering activities as a result of memory and cognitive impairment. Therefore, in this research paper, a watch-type device (Smart Watch), server system, and step detection algorithm utilizing a 3-axis acceleration sensor are developed. The suggested step detection algorithm showed an accuracy of 96% in verifying normal steps.


3-axis accelerometer u-health step number detection algorithm 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Tabert, M. H., Liu, X., Doty, R.,L., Serby, M., Zamora, D., Pelton, G.H., Marder, K., Albers, M.W., Stern, Y., Devanand, D.P.: A 10-item smell identification scale related to risk for Alzheimer’s disease. Annals of Neurology 58(1), 155–160 (2005)CrossRefGoogle Scholar
  2. 2.
    Company Keruve (2008),
  3. 3.
    u-safe Gang-nam (2009),
  4. 4.
    kt i-search (2009),
  5. 5.
    Yang, J.: Toward Physical Activity Diary: Motion Recognition Using Simple Acceleration Features with Mobile Phones. In: IMCE-2009, pp. 1–10 (2009)Google Scholar
  6. 6.
    Bao, L., Intille, S.S.: Activity Recognition from User-Annotated Acceleration Data. In: Ferscha, A., Mattern, F. (eds.) PERVASIVE 2004. LNCS, vol. 3001, pp. 1–17. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  7. 7.
    Baek, J., Lee, G., Park, W., Yun, B.-J.: Accelerometer Signal Processing for User Activity Detection. In: Negoita, M.G., Howlett, R.J., Jain, L.C. (eds.) KES 2004. LNCS (LNAI), vol. 3215, pp. 610–617. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  8. 8.
    Ravi, N., Dandekar, N., Mysore, P., Littman, M.L.: Activity Recognition from Accelerometer Data. In: Proceeding of the National Conference on Artificial Intelligence, vol. 20(3), pp. 1541–1546 (2005)Google Scholar
  9. 9.
    Yoo, H.-M., Suh, J.-W., Cha, E.-J., Bae, H.-D.: Walking Number Detection Algorithm using a 3-axial Accelerometer Sensor adn Activity monitoring. Korea Contents 8(8), 253–260 (2008)CrossRefGoogle Scholar
  10. 10.
    Shin, S.H., Park, C.G.: Adaptive Step Length Estimation Algorithm Using Low-Cost MEMS Inertial Sensors. In: IEEE Sensors Applications Symposium, San Diego, California, USA, pp. 1–5 (2007)Google Scholar
  11. 11.
    Noh, Y.-H., Ye, S.-Y., Jeong, D.-U.: System Implementation and Algorithm Development for Classification of the Activity States Using 3 Axial Accelerometer. J. KIEEME 24(1), 81–88 (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Department of Computer EngineeringSejong UniversitySeoulKorea

Personalised recommendations