FALL DETECTION SYSTEM FOR ELDERLY PEOPLE - A Neural Network Approach

Getúlio Igrejas, Joana S. Amaral, Pedro J. Rodrigues

Abstract

In this work a new approach for a fall detection system is proposed. The device integrates a 3-axis accelerometer and a 3-axis gyroscope to measure linear acceleration and angular velocities, respectively. Information from both sensors is used to characterize movements through selected features extracted from raw data. A classification system based on a Feedforward Backpropagation Neural Network is then trained, based on the extracted features. The performed tests present low false positives and low false negatives rates with good specificity and sensitivity values.

References

  1. L., Vanzago, L., 2010. Accelerometer-based fall detection using optimized ZigBee data streaming. Microelectronics Journal, 41(11), 703-710
  2. Bianchi, F., Redmond, S. J., Narayanan, M. R., Cerutti, S., Lovell, N.H., 2010. Barometric Pressure and Triaxial Accelerometry-Based Falls Event Detection. Ieee Transactions on Neural Systems and Rehabilitation Engineering, 18(6), 619-627.
  3. Bourke, A. K., O'Brien, J. V., Lyons, G. M., 2007a. Evaluation of a threshold-based tri-axial accelerometer fall detection algorithm. Gait & Posture, 26(2), 194-199.
  4. Bourke, A. K., van de Ven, P., Gamble, M., O'Connor, R., Murphy, K., Bogan, E., McQuade, E., Finucane, P., ÓLaighin, G. Nelson, J., 2010b. Assessment of Waistworn Tri-axial Accelerometer Based Fall-detection Algorithms using Continuous Unsupervised Activities. 2010 Annual International Conference of the Ieee Engineering in Medicine and Biology Society (Embc), 2782-2785.
  5. CDC. (2011, December 8 2010). Falls among older Adults: an overview. Retrieved 04-06-2011, from http://www.cdc.gov/HomeandRecreationalSafety/Falls /adultfalls.html
  6. Hausdorff, J. M., Rios, D. A., Edelberg, H. K., 2001. Gait variability and fall risk in community-living older adults: A 1-year prospective study. Archives of Physical Medicine and Rehabilitation, 82, 1050-1056.
  7. Hornbrook, M. C., Stevens, V. J., Wingfield, D. J., Hollis, J. F., Greenlick, M. R., Ory, M. G., 1994. Preventing Falls among Community-Dwelling Older Persons - Results from a Randomized Trial. Gerontologist, 34(1), 16-23.
  8. Kangas, M., Konttila, A., Lindgren, P., Winblad, I., & Jamsa, T., 2008. Comparison of low-complexity fall detection, algorithms for body attached accelerometers. Gait & Posture, 28(2), 285-291.
  9. Laguna, M. A., Marques, J. M., Tirado, M. J., Finat, J., 2010. Fall detection system: a solution based on low cost sensors. Systems and Information Technologies, 89-94.
  10. Noury, N., Rumeau, P., Bourke, A. K., Olaighin, G., Lundy, J. E., 2008. A proposal for the classification and evaluation of fall detectors. Irbm, 29(6), 340-349.
  11. Nyan, M. N., Tay, F. E. H., Murugasu, E., 2008. A wearable system for pre-impact fall detection. Journal of Biomechanics, 41(16), 3475-3481.
  12. Rumelhart, D. E., Hinton, G. E., & Williams, R. J., 1986. Learning Representations by Back-Propagating Errors. Nature, 323(6088), 533-536.
Download


Paper Citation


in Harvard Style

Igrejas G., S. Amaral J. and J. Rodrigues P. (2012). FALL DETECTION SYSTEM FOR ELDERLY PEOPLE - A Neural Network Approach . In Proceedings of the International Conference on Biomedical Electronics and Devices - Volume 1: BIODEVICES, (BIOSTEC 2012) ISBN 978-989-8425-91-1, pages 355-358. DOI: 10.5220/0003792003550358


in Bibtex Style

@conference{biodevices12,
author={Getúlio Igrejas and Joana S. Amaral and Pedro J. Rodrigues},
title={FALL DETECTION SYSTEM FOR ELDERLY PEOPLE - A Neural Network Approach},
booktitle={Proceedings of the International Conference on Biomedical Electronics and Devices - Volume 1: BIODEVICES, (BIOSTEC 2012)},
year={2012},
pages={355-358},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003792003550358},
isbn={978-989-8425-91-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Biomedical Electronics and Devices - Volume 1: BIODEVICES, (BIOSTEC 2012)
TI - FALL DETECTION SYSTEM FOR ELDERLY PEOPLE - A Neural Network Approach
SN - 978-989-8425-91-1
AU - Igrejas G.
AU - S. Amaral J.
AU - J. Rodrigues P.
PY - 2012
SP - 355
EP - 358
DO - 10.5220/0003792003550358