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

Author

Listed:
  • Alla R. Kammerdiner

    (New Mexico State University)

  • Andre N. Guererro

    (New Mexico State University)

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.

Suggested Citation

  • Alla R. Kammerdiner & Andre N. Guererro, 2019. "Data-driven combinatorial optimization for sensor-based assessment of near falls," Annals of Operations Research, Springer, vol. 276(1), pages 137-153, May.
    • Handle: RePEc:spr:annopr:v:276:y:2019:i:1:d:10.1007_s10479-017-2585-1
    DOI: 10.1007/s10479-017-2585-1
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    References listed on IDEAS

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    1. Dario Martelli & Fiorenzo Artoni & Vito Monaco & Angelo Maria Sabatini & Silvestro Micera, 2014. "Pre-Impact Fall Detection: Optimal Sensor Positioning Based on a Machine Learning Paradigm," PLOS ONE, Public Library of Science, vol. 9(3), pages 1-8, March.
    2. William P. Pierskalla, 1968. "Letter to the Editor—The Multidimensional Assignment Problem," Operations Research, INFORMS, vol. 16(2), pages 422-431, April.
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    Cited by:

    1. Alla Kammerdiner & Alexander Semenov & Eduardo L. Pasiliao, 2022. "Multidimensional Assignment Problem for Multipartite Entity Resolution," Journal of Global Optimization, Springer, vol. 84(2), pages 491-523, October.

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