Gait-Initiation Onset Estimation During Sit-to-Walk Comparing Healthy Individuals and Ambulatory Community-Dwelling Stroke Survivors

Dataset


Jones, GD, James, DC, Thacker, M, Perry, R and Green, DA (2019). Gait-Initiation Onset Estimation During Sit-to-Walk Comparing Healthy Individuals and Ambulatory Community-Dwelling Stroke Survivors. doi:10.18744/LSBU.002933
AuthorsJones, GD, James, DC, Thacker, M, Perry, R and Green, DA
Abstract

These data consist of measured variables derived from Visual 3D software (C-Motion Inc., Germantown, USA) for trials undertaken by 20 stroke subjects and 21 gender and age-matched healthy subjects performing 2 tasks (sit-to-walk [STW] and sit-to-stand [STS]) 5 times. Subjects began walking with their affected lower extremity (stroke group) or their non-dominant lower extremity (NonDom; healthy group).

Keywordsstroke; gait; sit-to-walk; sit-to-stand
Year2019
Digital Object Identifier (DOI)doi:10.18744/LSBU.002933
Data files
License
CC BY 4.0
Data type
Spreadsheet
File Access Level
Open
Data files
License
CC BY 4.0
Data type
Text
Contents
README
File Access Level
Open
Data collection period01 Dec 2016 to end of 28 Feb 2017
Data collection method

Subjects were asked to perform STW and STS on 5 occasions each, in a randomised order during one measurement session. On each occasion subjects followed a novel low-risk protocol. They were instructed in all trials, upon illumination of a light signal, to stand and walk forward (having led with their affected (stroke) or non-dominant (healthy) leg) ~5m along a walkway at a comfortable pace, stop and turn off the light using a switch. In the STS trials, subjects commenced walking having paused once upright.

A 3D whole-body marker set was used, which was defined by placing 40 reflective markers on skin overlying anatomical landmarks. Body segments were tracked using an additional 31 markers mounted in accordance with a six degrees-of-freedom marker-set. Kinematic data were acquired using 10 infrared cameras sampled at 60Hz and synchronised with the analogue output from the force plates and seat-mat .

Data preparation and processing activities

Raw marker trajectories and analogue data were imported into Visual3D software. Kinematic and kinetic data were processed with a 10 Hz and 25 Hz 4th order low-pass Butterworth filter, respectively. The pressure-mat analogue signal was filtered using 25-point window averaging in order to reproducibly determine seat-off.
Movement was analysed between events identifying the the start of rising (movement-onset) and the end of GI at the first toe-off (TO1). Each GI-Onset estimation method was calculated differently within this time-frame according to strict criteria.

Publication dates
Online07 Feb 2019
Publication process dates
Deposited25 Jul 2019
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https://openresearch.lsbu.ac.uk/item/87595

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