Research Study Abstract

Smartwatches Can Detect Walker and Cane Use in Older Adults

  • Published on April 22, 2019

Background and Objectives
Clinicians commonly prescribe assistive devices such as walkers or canes to reduce older adults’ fall risk. However, older adults may not consistently use their assistive device, and measuring adherence can be challenging due to self-report bias or cognitive deficits. Because walking patterns can change while using an assistive device, we hypothesized that smartphones and smartwatches, combined with machine-learning algorithms, could detect whether an older adult was walking with an assistive device.

Research Design and Methods
Older adults at an Adult Day Center (n = 14) wore an Android smartphone and ActiGraph smartwatch while completing the six-minute walk, 10-meter walk, and Timed Up and Go tests with and without their assistive device on five separate days. We used accelerometer data from the devices to build machine-learning algorithms to detect whether the participant was walking with or without their assistive device. We tested our algorithms using cross-validation.

Smartwatch classifiers could accurately detect assistive device use, but smartphone classifiers performed poorly. Customized smartwatch classifiers, which were created specifically for one participant, had greater than 95% classification accuracy for all participants. Noncustomized smartwatch classifiers (ie, an “off-the-shelf” system) had greater than 90% accuracy for 10 of the 14 participants. A noncustomized system performed better for walker users than cane users.

Discussion and Implications
Our approach can leverage data from existing commercial devices to provide a deeper understanding of walker or cane use. This work can inform scalable public health monitoring tools to quantify assistive device adherence and enable proactive fall interventions.


  • Stephen A. Antos, PT, DPT 1,2,3
  • Margaret K. Danilovich, PT, DPT, PhD 2
  • Amy R. Eisenstein, PhD 4,5
  • Keith E. Gordon, PhD 2,6
  • Konrad P. Kording, PhD 3,7


  • 1

    Department of Biomedical Engineering, Northwestern University, Evanston, Illinois

  • 2

    Department of Physical Therapy and Human Movement Sciences, Northwestern University, Chicago, Illinois

  • 3

    Department of Bioengineering, University of Pennsylvania, Philadelphia

  • 4

    Department of Medical Social Sciences, Northwestern University, Chicago, Illinois

  • 5

    CJE SeniorLife, Leonard Schanfield Research Institute, Chicago, Illinois

  • 6

    Research Service, Edward Hines Jr. VA Hospital, Hines, Illinois

  • 7

    Department of Neuroscience, University of Pennsylvania, Philadelphia


Innovation in Aging

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