Advertisement

A Noninvasive Sensor System for Discriminating Mobility Pattern on a Bed

  • Seung Ho Cho
  • Seokhyang ChoEmail author
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 339)

Abstract

In this paper, we propose a noninvasive sensor system for discriminating mobility pattern of a resident on the bed without inconvenience. The proposed system consists of a thin and wide film style of piezoelectric force senor, a signal processing board, and data collecting program. There are four different types of motion that were simulated by non-patient volunteers. About 10,000 experimental motions of subjects were performed. Sensor data by human motions were collected and preprocessed by a moving average filter, transformed by FFT, and classified by the k-NN algorithm with k = 1. The experiment yielded the overall discrimination rate of 89.4 %. The proposed system will contribute to differentiating mobility pattern on a bed and distinguishing the physical characteristics of a person.

Keywords

u-Health care Noninvasive sensor Mobility pattern Motion recognition Ubiquitous computing 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Song, T.: u-Health: Current Status and Policy. Korea Institute of Health and Social Affairs (KIHASA), Republic of Korea (2011)Google Scholar
  2. 2.
    Gaddam, A., Mukhopadhyay, S.C. and Sen Gupta, G.: Necessity of a Bed-Sensor in a Smart Digital Home to Care for Elder-People. Sensors, IEEE (2008) 1340-1343Google Scholar
  3. 3.
  4. 4.
    Gaddam, A., Kaur, K., Sen Gupta, G., and Mukhopadhyay, S.C.: Determination of Sleep Quality of Inhabitant in a Smart Home using an Intelligent Bed Sensing System. In: IEEE Instrumentation and Measurement Technology Conference (I2MTC), Austin (2010) 1613-1617Google Scholar
  5. 5.
    Holtzman, M., Townsend, D., Goubran R., and Knoefel, F.: Validation of Pressure Sensors for Physiological Monitoring in Home Environments. In: ’10 IEEE International Workshop on Medical Measurements and Applications Proceedings (MeMeA), Canada (2010) 38-42Google Scholar
  6. 6.
    Borazio, M., Van Laerhoven, K.: Combining Wearable and Environmental Sensing into an Unobtrusive Tool for Long-Term Sleep Studies. In: IHI ’12 Proc. of the 2nd ACM SIGHIT International Health Informatics Symposium, ACM, Miami (2012) 71-80Google Scholar
  7. 7.
    Cho, S., Phillips, W.D., Sankar, R., and Moon, B.: A State Preserving Approach to Recognizing Human Behavior using Wireless Infrared and Vibration Sensors. Proc. of IEEE Southeastcon (2012) 1-6Google Scholar
  8. 8.
    Cooley, J.W. and Tukey, J.W.: An Algorithm for the Machine Calculation of Complex Fourier Series. Mathematics of Computation, 19 (1965) 297-301Google Scholar
  9. 9.
    Hyounkyo Oh, et al., Preprocessing in a Noninvasive Sensor System. Proc. of KIPS Spring Conference, 20(1), Republic of Korea (2013) 83-85Google Scholar
  10. 10.
    Cover, T. and Hart, P.: Nearest Neighbor Pattern Classification. IEEE Transactions on Information Theory 13(1) (1967) 21-27Google Scholar
  11. 11.
    Hamilton, BB., Granger CV., Sherwin, FS. et al.: A Uniform National Data System for Medical Rehabilitation, Rehabilitation Outcomes. Analysis and Measurement, Brookes Pub. (1987) 137-147Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.Division of Computer and Media Information EngineeringKangnam UniversityGiheung-gu, Yongin-siRepublic of Korea
  2. 2.School of Information and Communication EngineeringSungkyunkwan UniversityJangan-gu, Suwon-siRepublic of Korea

Personalised recommendations