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.

http://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.

http://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

http://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.

http://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.

http://eprints.bournemouth.ac.uk/34954/

Source: Europe PubMed Central