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Engineering Human Gait and the Potential Role of Wearable Sensors to Monitor Falls

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Falls and Cognition in Older Persons

Abstract

Falls and falls related injuries are the major causes of non-fatal injuries in older adults. With recent advances in mathematics, science and technology, many scientists and engineers are devoting their efforts to prevent falls or to diminish the negative health outcomes after falls. In this chapter, we briefly review major engineering approaches to recover or augment the human gait function pre- and post-falls. Given the proliferation of wearable sensors and the availability of computational resources in the last decade, we focused on the role of wearable sensors to monitor gait instabilities and potentially prevent falls. We reviewed the general framework for gait monitoring using wearables and its utility in real-life settings such as homes or retirement communities. In the last part of the chapter, we focused on recent contributions that have proposed wearable sensors for gait monitoring and fall inferences.

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Notes

  1. 1.

    www.gaitup.com

  2. 2.

    www.kinesis.ie

  3. 3.

    These data will be stored as metadata describing and giving information about the IMU sensor data.

  4. 4.

    Usually, the fall will be predefined by a protocol. Development work will involve static falls, that is, fall from a standing position with variations in how the volunteer falls. Additionally, protocols may ask the volunteer to simulate a trip, near fall or fall during a walking task or fall when arising from a chair to mimic a real-world, free-living fall event.

  5. 5.

    Berg Balance Scale

  6. 6.

    http://farseeingresearch.eu/the-farseeing-real-world-fall-repository-a-large-scale-collaborative-database-to-collect-and-share-sensor-signals-from-real-world-falls

  7. 7.

    https://cordis.europa.eu/project/rcn/101785_en.html

  8. 8.

    ProFaNE: www.profane.eu.org

  9. 9.

    E.G. https://activeprotective.com/

  10. 10.

    For gait and falls but extends to all aspects of wearable measurement

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Swanson School of Engineering, Department of Bioengineering, Swanson School of Engineering, Department of Biomedical Informatics, School of Medicine, Intelligent Systems Program, School of Computing and Information, University of Pittsburgh, Pittsburgh, PA, USA

    Ervin Sejdić

  2. Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne, UK

    Alan Godfrey

  3. Department of Kinesiology, University of Waterloo, Waterloo, ON, Canada

    William McIlroy

  4. Departments of Medicine (Geriatric Medicine), and Epidemiology and Biostatistics, Schulich School of Medicine & Dentistry, University of Western Ontario, London, ON, Canada

    Manuel Montero-Odasso

  5. Gait and Brain Lab, Parkwood Institute, Lawson Health Research Institute, London, ON, Canada

    Manuel Montero-Odasso

Authors
  1. Ervin Sejdić
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  2. Alan Godfrey
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  3. William McIlroy
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  4. Manuel Montero-Odasso
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Correspondence to Ervin Sejdić .

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Editors and Affiliations

  1. Division of Geriatric Medicine, University of Western Ontario, London, ON, Canada

    Manuel Montero-Odasso

  2. Division of Neurology, University of Alberta, Edmonton, AB, Canada

    Richard Camicioli

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Sejdić, E., Godfrey, A., McIlroy, W., Montero-Odasso, M. (2020). Engineering Human Gait and the Potential Role of Wearable Sensors to Monitor Falls. In: Montero-Odasso, M., Camicioli, R. (eds) Falls and Cognition in Older Persons. Springer, Cham. https://doi.org/10.1007/978-3-030-24233-6_22

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  • DOI: https://doi.org/10.1007/978-3-030-24233-6_22

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