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A practical step length algorithm using lower limb angular velocities

Overview of attention for article published in Journal of Biomechanics, November 2017
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (80th percentile)
  • High Attention Score compared to outputs of the same age and source (85th percentile)

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Article details
Title
A practical step length algorithm using lower limb angular velocities
Published in
Journal of Biomechanics, November 2017
DOI 10.1016/j.jbiomech.2017.11.010
Pubmed ID
Authors
Abstract

The use of Inertial Measurement Units (IMUs) for spatial gait analysis has opened the door to unconstrained measurements within the home and community. Bandwidth, cost limitations, and ease of use has historically restricted the number and location of sensors worn on the body. In this paper, we describe a four-sensor configuration of IMUs placed on the shanks and thighs that is sufficient to provide an accurate measure of temporal gait parameters, spatial gait parameters, and joint angle dynamics during ambulation. Estimating spatial gait parameters solely from gyroscope data is preferred because gyroscopes are less susceptible to sensor noise and a system comprised of only gyroscopes uses decreased bandwidth compared to a typical 9 degree-of-freedom IMU. The purpose of this study was to determine the validity of a novel method of step length estimation using gyroscopes attached to the shanks and thighs. An Inverted Pendulum Model algorithm (IPM) was proposed to calculate step length, stride length, and gait speed. The algorithm incorporates heel-strike events and average forward velocity per step to make these assessments. IMU algorithm accuracy was determined via concurrent validity with an instrumented walkway and results explained via the collision model of gait. The IPM produced accurate estimates of step length, stride length, and gait speed with a mean difference of 3 cm and an RMSE of 6.6 cm for step length, thus establishing a new approach for spatial gait parameter calculation. The lack of numerical integration in IPM makes it well suited for use in continuous monitoring applications where sensor sampling rates are restricted.

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X Demographics

X Demographics

The data shown below were collected from the profiles of 15 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 136 Mendeley readers of this research output. Click here to see the associated Mendeley record.
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Geographical breakdown

Geographical breakdown
Country Count As %
Unknown 136 100%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Researcher 25 18%
Student > Ph. D. Student 24 18%
Student > Master 14 10%
Student > Doctoral Student 10 7%
Student > Bachelor 7 5%
Other 11 8%
Unknown 45 33%
Readers by discipline
Readers by discipline Count As %
Engineering 39 29%
Medicine and Dentistry 11 8%
Neuroscience 8 6%
Computer Science 6 4%
Sports and Recreations 4 3%
Other 12 9%
Unknown 56 41%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 9. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 18 December 2017.
All research outputs
#5,128,001
of 30,878,141 outputs
Outputs from Journal of Biomechanics
#832
of 6,081 outputs
Outputs of similar age
#65,933
of 332,496 outputs
Outputs of similar age from Journal of Biomechanics
#17
of 118 outputs
Altmetric has tracked 30,878,141 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 6,081 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.8. This one has done well, scoring higher than 86% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 332,496 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 80% of its contemporaries.
We're also able to compare this research output to 118 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 85% of its contemporaries.