Skip to main content
SpringerLink
Log in

Data-driven combinatorial optimization for sensor-based assessment of near falls

  • Computational Biomedicine
  • Published:
Annals of Operations Research Aims and scope Submit manuscript

Abstract

Falls represent a considerable public health problem, especially in older population. We describe and evaluate data-driven operations research models for detection and situational assessment of falls and near falls with a system of wearable sensors. The models are formulated as instances of the multidimensional assignment problem. Our computational studies provide some initial empirical evidence of the potential usefulness of this new application of the multidimensional assignment problem.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

References

  • Aziz, O., Park, E. J., Mori, G., & Robinovitch, S. N. (2014). Distinguishing the causes of falls in humans using an array of wearable tri-axial accelerometers. Gait & Posture, 39(1), 506–512.

    Article  Google Scholar 

  • Aziz, O., & Robinovitch, S. N. (2011). An analysis of the accuracy of wearable sensors for classifying the causes of falls in humans. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 19(6), 670–676.

    Article  Google Scholar 

  • Centers for Disease Control and Prevention, National Center for Injury Prevention and Control, Division of Unintentional Injury Prevention. Important Facts about Falls. http://www.cdc.gov/homeandrecreationalsafety/falls/adultfalls.html (online) Accessed January 21, 2016

  • Centers for Disease Control and Prevention, National Center for Injury Prevention and Control. Webbased Injury Statistics Query and Reporting System (WISQARS). http://www.cdc.gov/injury/wisqars/ (online) Accessed August 15, 2013.

  • Giansanti, D., Macellari, V., & Maccioni, G. (2008). New neural network classifier of fall-risk based on the Mahalanobis distance and kinematic parameters assessed by a wearable device. Physiological Measurement, 29, N11–N19.

    Article  Google Scholar 

  • Kammerdiner, A. (2015). Ranking risk exposures for situational surveillance of falls with sensors. Operations Research for Health Care, 7, 132–137.

    Article  Google Scholar 

  • Kammerdiner, A. R. (2008). Multidimensional assignment problem. In: Encyclopedia of Optimization, Springer, pp. 2396–2402.

  • Li, Q., Stankovic, J. A., Hanson, M. A., Barth, A. T., Lach, J., & Zhou, G. (2009). Accurate, fast fall detection using gyroscopes and accelerometer-derived posture information. In: Sixth International Workshop on Wearable and Implantable Body Sensor Networks, 2009. BSN 2009, IEEE, pp. 138–143.

  • Maidan, I., Freedman, T., Tzemah, R., Giladi, N., Mirelman, A., & Hausdorff, J. M. (2014). Introducing a new definition of a near fall: Intra-rater and inter-rater reliability. Gait & Posture, 39(1), 645–647.

    Article  Google Scholar 

  • Martelli, D., Artoni, F., Monaco, V., Sabatini, A. M., & Micera, S. (2014). Pre-impact fall detection: Optimal sensor positioning based on a machine learning paradigm. PloS one, 9(3), e92037.

    Article  Google Scholar 

  • Myford, C. M., & Wolfe, E. W. (2003). Detecting and measuring rater effects using many-facet Rasch measurement: Part I. Journal of Applied Measurement, 4(4), 386–422.

    Google Scholar 

  • Noury, N., Fleury, A., Rumeau, P., Bourke, A. K., Laighin, G. O., Rialle, V., Lundy, J. E. (2007) Fall detection-principles and methods. In 29th Annual International Conference of the IEEE On Engineering in Medicine and Biology Society, 2007. EMBS 2007, IEEE, pp. 1663–1666.

  • Pierskalla, W. P. (1968). The multidimensional assignment problem. Operations Research, 16, 422–431.

    Article  Google Scholar 

  • Roch, S. G., Woehr, D. J., Mishra, V., & Kieszczynska, U. (2012). Rater training revisited: An updated meta-analytic review of frame-of-reference training. Journal of Occupational and Organizational Psychology, 85(2), 370–395.

    Article  Google Scholar 

  • Shany, T., Redmond, S. J., Narayanan, M. R., & Lovell, N. H. (2012). Sensors-based wearable systems for monitoring of human movement and falls. IEEE Sensors Journal, 12(3), 658–670.

    Article  Google Scholar 

  • Stevens, J. A., Corso, P. S., Finkelstein, E. A., & Miller, T. R. (2006). The costs of fatal and non-fatal falls among older adults. Injury Prevention, 12(5), 290–295.

    Article  Google Scholar 

  • Targum, S. D. (2006). Evaluating rater competency for CNS clinical trials. Journal of Clinical Psychopharmacology, 26(3), 308–310.

    Article  Google Scholar 

  • United Nations, Department of Economic and Social Affairs, Population Division. World Population Prospects: The 2010, Volume II: Demographic Profiles. http://esa.un.org/wpp/country-profiles/pdf/840.pdf(online) Accessed on April 23, (2013)

  • World Health Organization. Are you ready? What you need to know about ageing. World Health Day 2012—Toolkit for event organizers. http://www.who.int/world-health-day/2012/toolkit/background/en/ (online) Accessed on January 21, 2016.

  • Yang, C. C., & Hsu, Y. L. (2010). A review of accelerometry-based wearable motion detectors for physical activity monitoring. Sensors, 10(8), 7772–7788.

    Article  Google Scholar 

Download references

Acknowledgements

The authors gratefully acknowledge the support from the National Science Foundation grant EEC-1342415.

Author information

Authors and Affiliations

  1. New Mexico State University, P.O. Box 30001, MSC 4230, Las Cruces, NM, 88003, USA

    Alla R. Kammerdiner & Andre N. Guererro

Authors
  1. Alla R. Kammerdiner
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Andre N. Guererro
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alla R. Kammerdiner.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kammerdiner, A.R., Guererro, A.N. Data-driven combinatorial optimization for sensor-based assessment of near falls. Ann Oper Res 276, 137–153 (2019). https://doi.org/10.1007/s10479-017-2585-1

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10479-017-2585-1

Keywords

Search

Navigation