Literature DB >> 7730393

Footswitch system for measurement of the temporal parameters of gait.

J M Hausdorff1, Z Ladin, J Y Wei.   

Abstract

Gait analysis relies upon accurate measurement of initial and end foot contact times. These times act as a reference point for correlating all other gait data and as a mean of distinguishing normal and pathologic gait. We have developed a simple, inexpensive footswitch system that provides accurate estimates of the start and end of stance phase for sequential steps. The estimates of the beginning and end of stance phase do not require custom footwear, extensive calibration, or precise placement of the sensor within the shoe. The system is based on a commercially available transducer and can be readily reproduced for use in a laboratory setting for less than $50. We describe this system, as well as its validation. To assess the accuracy of this footswitch system, we compared footswitch based estimates of initial and end foot contact times with those obtained using a force platform as 10 people took 30 steps (10 each at slow, normal and fast walking rates) across a force platform. Both estimates coincided within +/- 10 ms (mean: 0 +/- 3 ms; N = 300) for the start of stance phase and within +/- 22 ms (mean: -1 +/- 8 ms; N = 300) for the end of stance phase. For stance duration, the differences ranged from -24 to 28 ms (mean: 1 +/- 10 ms; N = 300). In combination, these measures can be used to estimate stance duration to within 3% of force plate determined values for steps with stance durations ranging from 446 to 1594 ms. Estimates of swing and stride duration also are within 5% of force plate determined values. This system should therefore prove to be a useful tool for augmenting laboratory based investigations of gait.

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Year:  1995        PMID: 7730393     DOI: 10.1016/0021-9290(94)00074-e

Source DB:  PubMed          Journal:  J Biomech        ISSN: 0021-9290            Impact factor:   2.712


  34 in total

1.  Phase determination during normal running using kinematic data.

Authors:  A Hreljac; N Stergiou
Journal:  Med Biol Eng Comput       Date:  2000-09       Impact factor: 2.602

2.  Pedaling time variability is increased in dropped riding position.

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Journal:  Eur J Appl Physiol       Date:  2011-12-20       Impact factor: 3.078

3.  Portable activity monitoring system for temporal parameters of gait cycles.

Authors:  Jung-Ah Lee; Sang-Hyun Cho; Young-Jae Lee; Heui-Kyung Yang; Jeong-Whan Lee
Journal:  J Med Syst       Date:  2009-06-16       Impact factor: 4.460

Review 4.  Gait dynamics, fractals and falls: finding meaning in the stride-to-stride fluctuations of human walking.

Authors:  Jeffrey M Hausdorff
Journal:  Hum Mov Sci       Date:  2007-07-05       Impact factor: 2.161

5.  Two simple methods for determining gait events during treadmill and overground walking using kinematic data.

Authors:  J A Zeni; J G Richards; J S Higginson
Journal:  Gait Posture       Date:  2007-08-27       Impact factor: 2.840

Review 6.  Gait dynamics in Parkinson's disease: common and distinct behavior among stride length, gait variability, and fractal-like scaling.

Authors:  Jeffrey M Hausdorff
Journal:  Chaos       Date:  2009-06       Impact factor: 3.642

7.  A novel approach for analysis of altered gait variability in amyotrophic lateral sclerosis.

Authors:  Yi Xia; Qingwei Gao; Yixiang Lu; Qiang Ye
Journal:  Med Biol Eng Comput       Date:  2015-10-30       Impact factor: 2.602

Review 8.  Mobility assessment in older people: new possibilities and challenges.

Authors:  Wiebren Zijlstra; Kamiar Aminian
Journal:  Eur J Ageing       Date:  2007-02-06

9.  Impaired regulation of stride variability in Parkinson's disease subjects with freezing of gait.

Authors:  J M Hausdorff; J D Schaafsma; Y Balash; A L Bartels; T Gurevich; N Giladi
Journal:  Exp Brain Res       Date:  2003-01-22       Impact factor: 1.972

10.  Spatial variability during gait initiation while dual tasking is increased in individuals with mild cognitive impairment.

Authors:  S Boripuntakul; S R Lord; M A D Brodie; S T Smith; P Methapatara; N Wongpakaran; S Sungkarat
Journal:  J Nutr Health Aging       Date:  2014-03       Impact factor: 4.075

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