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

  • GD Jones (Creator)
  • Darren C James (Contributor)
  • Micheal Thacker (Contributor)
  • R Perry (Contributor)
  • DA Green (Contributor)

Dataset

Description

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).
Date made available25 Jul 2019
Date of data production1 Dec 2016 - 28 Feb 2017

Keywords for datasets

  • Keyword
  • stroke
  • gait
  • sit-to-walk
  • sit-to-stand

Data Collection Method

  • Description
  • 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

  • Description
  • 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.
  • Review. Data underpins published article at https://doi.org/10.1371/journal.pone.0217563 : presume to keep data indefinitely

Cite this