Lower limb kinematic, kinetic and spatial-temporal gait data for healthy adults using a self-paced treadmill
Authors: Bahadori, S., Williams, J.M. and Wainwright, T.W.
Journal: Data in Brief
Volume: 34
eISSN: 2352-3409
DOI: 10.1016/j.dib.2020.106613
Abstract:Through gait analysis, gait phases can be identified, the kinematic and kinetic parameters of human gait events can be determined, and quantitative evaluation can be undertaken. This data article is the first to report a comprehensive data set on a large cohort of healthy participants. Individual strides were determined from vertical force data and all kinematics and kinetic data separated into strides. Local minima and maxima were determined respectively for each anatomical region and the mean calculated for twenty of the 25 strides. When twenty strides were not available the mean of ten strides was used. Stride data were time normalised so one stride was represented by 100%. In addition to the local maxima and minima, the kinematic- and kinetic-time curves were explored and used to determine the mean kinematic-time and kinetic-time curves across all trials and participants (∼1800 gait cycles) to provide mean±sd kinematic- and kinetic-time curves for each of the anatomical regions.
https://eprints.bournemouth.ac.uk/34954/
Source: Scopus
Lower limb kinematic, kinetic and spatial-temporal gait data for healthy adults using a self-paced treadmill.
Authors: Bahadori, S., Williams, J.M. and Wainwright, T.W.
Journal: Data Brief
Volume: 34
Pages: 106613
eISSN: 2352-3409
DOI: 10.1016/j.dib.2020.106613
Abstract:Through gait analysis, gait phases can be identified, the kinematic and kinetic parameters of human gait events can be determined, and quantitative evaluation can be undertaken. This data article is the first to report a comprehensive data set on a large cohort of healthy participants. Individual strides were determined from vertical force data and all kinematics and kinetic data separated into strides. Local minima and maxima were determined respectively for each anatomical region and the mean calculated for twenty of the 25 strides. When twenty strides were not available the mean of ten strides was used. Stride data were time normalised so one stride was represented by 100%. In addition to the local maxima and minima, the kinematic- and kinetic-time curves were explored and used to determine the mean kinematic-time and kinetic-time curves across all trials and participants (∼1800 gait cycles) to provide mean±sd kinematic- and kinetic-time curves for each of the anatomical regions.
https://eprints.bournemouth.ac.uk/34954/
Source: PubMed
Lower limb kinematic, kinetic and spatial-temporal gait data for healthy adults using a self-paced treadmill
Authors: Bahadori, S., Williams, J.M. and Wainwright, T.W.
Journal: DATA IN BRIEF
Volume: 34
ISSN: 2352-3409
DOI: 10.1016/j.dib.2020.106613
https://eprints.bournemouth.ac.uk/34954/
Source: Web of Science (Lite)
Lower Limb Kinematic, Kinetic and Spatial-temporal Gait Data for Healthy Adults Using a Self-paced Treadmill
Authors: Bahadori, S., Williams, J. and Wainwright, T.
Journal: Data in Brief
Publisher: Elsevier
ISSN: 2352-3409
DOI: 10.1016/j.dib.2020.106613
Abstract:Through gait analysis, gait phases can be identified, the kinematic and kinetic parameters of human gait events can be determined, and quantitative evaluation can be undertaken. This data article is the first to report a comprehensive data set on a large cohort of healthy participants. Individual strides were determined from vertical force data and all kinematics and kinetic data separated into strides. Local minima and maxima were determined respectively for each anatomical region and the mean calculated for twenty of the 25 strides. When twenty strides were not available the mean of ten strides was used. Stride data were time normalised so one stride was represented by 100%. In addition to the local maxima and minima, the kinematic- and kinetic-time curves were explored and used to determine the mean kinematic-time and kinetic-time curves across all trials and participants (∼1800 gait cycles) to provide mean±sd kinematic- and kinetic-time curves for each of the anatomical regions.
https://eprints.bournemouth.ac.uk/34954/
Source: Manual
Lower limb kinematic, kinetic and spatial-temporal gait data for healthy adults using a self-paced treadmill.
Authors: Bahadori, S., Williams, J.M. and Wainwright, T.W.
Journal: Data in brief
Volume: 34
Pages: 106613
eISSN: 2352-3409
ISSN: 2352-3409
DOI: 10.1016/j.dib.2020.106613
Abstract:Through gait analysis, gait phases can be identified, the kinematic and kinetic parameters of human gait events can be determined, and quantitative evaluation can be undertaken. This data article is the first to report a comprehensive data set on a large cohort of healthy participants. Individual strides were determined from vertical force data and all kinematics and kinetic data separated into strides. Local minima and maxima were determined respectively for each anatomical region and the mean calculated for twenty of the 25 strides. When twenty strides were not available the mean of ten strides was used. Stride data were time normalised so one stride was represented by 100%. In addition to the local maxima and minima, the kinematic- and kinetic-time curves were explored and used to determine the mean kinematic-time and kinetic-time curves across all trials and participants (∼1800 gait cycles) to provide mean±sd kinematic- and kinetic-time curves for each of the anatomical regions.
https://eprints.bournemouth.ac.uk/34954/
Source: Europe PubMed Central
Lower Limb Kinematic, Kinetic and Spatial-temporal Gait Data for Healthy Adults Using a Self-paced Treadmill
Authors: Bahadori, S., Williams, J.M. and Wainwright, T.W.
Journal: Data in Brief
Volume: 34
Issue: February
ISSN: 2352-3409
Abstract:Through gait analysis, gait phases can be identified, the kinematic and kinetic parameters of human gait events can be determined, and quantitative evaluation can be undertaken. This data article is the first to report a comprehensive data set on a large cohort of healthy participants. Individual strides were determined from vertical force data and all kinematics and kinetic data separated into strides. Local minima and maxima were determined respectively for each anatomical region and the mean calculated for twenty of the 25 strides. When twenty strides were not available the mean of ten strides was used. Stride data were time normalised so one stride was represented by 100%. In addition to the local maxima and minima, the kinematic- and kinetic-time curves were explored and used to determine the mean kinematic-time and kinetic-time curves across all trials and participants (∼1800 gait cycles) to provide mean±sd kinematic- and kinetic-time curves for each of the anatomical regions.
https://eprints.bournemouth.ac.uk/34954/
Source: BURO EPrints