Database of lower limb kinematics and electromyography during gait-related activities in able-bodied subjects

Robert V. Schulte; Erik C. Prinsen; Leendert Schaake; Robert P.G. Paassen; Marijke Zondag; Eline S. van Staveren; Mannes Poel; Jaap H. Buurke

doi:10.1038/s41597-023-02341-6

Database of lower limb kinematics and electromyography during gait-related activities in able-bodied subjects

Robert V. Schulte^*, Erik C. Prinsen^*, Leendert Schaake, Robert P.G. Paassen, Marijke Zondag, Eline S. van Staveren, Mannes Poel, Jaap H. Buurke

^*Corresponding author for this work

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Abstract

This data descriptor describes the Roessingh Research & Development-MyLeg database for activity prediction (MyPredict), containing three data sets. These data sets contain data from 55 able-bodied subjects, mean age 24 ± 2 years, measured in 85 measurement sessions. Measurement sessions consisted of trials containing sitting, standing, overground walking, stair ascent, stair descent, ramp ascent, ramp descent, walking on uneven terrain and walking in simulated confined spaces. Subjects were measured using eight inertial measurement units in combination with different types of sEMG. Recorded kinematics consisted of joint angles, sensor accelerations, angular velocity, orientation and virtual marker positions. sEMG was recorded using bipolar sEMG, multi-array sEMG or a combination of both. All data showed excellent correlation with other online available data sets. The data reported in this descriptor forms a solid basis for research into myoelectric pattern recognition, myoelectric control development and electromyography to be used in data-driven applications.

Original language	English
Article number	461
Number of pages	10
Journal	Scientific Data
Volume	10
Early online date	14 Jul 2023
DOIs	https://doi.org/10.1038/s41597-023-02341-6
Publication status	Published - 14 Jul 2023

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10.1038/s41597-023-02341-6Licence: CC BY

ArticleFinal published version, 1.7 MBLicence: CC BY

Roessingh Research & Development-MyLeg database for activity prediction (MyPredict)
Schulte, R. (Creator), 4TU.Centre for Research Data, 26 May 2023
DOI: 10.4121/20418720, https://data.4tu.nl/datasets/01d30db7-95a8-4c39-afb9-4eb1a2f27539 and 2 more links, https://doi.org/10.4121/20418720.v1, https://data.4tu.nl/datasets/01d30db7-95a8-4c39-afb9-4eb1a2f27539/1 (show fewer)
Dataset

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@article{741940a35538479e872d57e2a9d89ecc,

title = "Database of lower limb kinematics and electromyography during gait-related activities in able-bodied subjects",

abstract = "This data descriptor describes the Roessingh Research & Development-MyLeg database for activity prediction (MyPredict), containing three data sets. These data sets contain data from 55 able-bodied subjects, mean age 24 ± 2 years, measured in 85 measurement sessions. Measurement sessions consisted of trials containing sitting, standing, overground walking, stair ascent, stair descent, ramp ascent, ramp descent, walking on uneven terrain and walking in simulated confined spaces. Subjects were measured using eight inertial measurement units in combination with different types of sEMG. Recorded kinematics consisted of joint angles, sensor accelerations, angular velocity, orientation and virtual marker positions. sEMG was recorded using bipolar sEMG, multi-array sEMG or a combination of both. All data showed excellent correlation with other online available data sets. The data reported in this descriptor forms a solid basis for research into myoelectric pattern recognition, myoelectric control development and electromyography to be used in data-driven applications.",

author = "Schulte, {Robert V.} and Prinsen, {Erik C.} and Leendert Schaake and Paassen, {Robert P.G.} and Marijke Zondag and Staveren, {Eline S. van} and Mannes Poel and Buurke, {Jaap H.}",

note = "Funding Information: This research is part of the MyLeg project and was funded by European Union{\textquoteright}s Horizon 2020 Research and Innovation Programme grant number 780871. The authors would like to thank Daniek Haarman, Eline Woudenberg, Ismael Mrad, Juliet Buma, Rick ter Brake and Rutger Zandvoort for their assistence during the measurements of MyPredict 2. Publisher Copyright: {\textcopyright} 2023, The Author(s).",

year = "2023",

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doi = "10.1038/s41597-023-02341-6",

language = "English",

volume = "10",

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issn = "2052-4463",

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AU - Schulte, Robert V.

AU - Prinsen, Erik C.

AU - Schaake, Leendert

AU - Paassen, Robert P.G.

AU - Zondag, Marijke

AU - Staveren, Eline S. van

AU - Poel, Mannes

AU - Buurke, Jaap H.

N1 - Funding Information: This research is part of the MyLeg project and was funded by European Union’s Horizon 2020 Research and Innovation Programme grant number 780871. The authors would like to thank Daniek Haarman, Eline Woudenberg, Ismael Mrad, Juliet Buma, Rick ter Brake and Rutger Zandvoort for their assistence during the measurements of MyPredict 2. Publisher Copyright: © 2023, The Author(s).

PY - 2023/7/14

Y1 - 2023/7/14

N2 - This data descriptor describes the Roessingh Research & Development-MyLeg database for activity prediction (MyPredict), containing three data sets. These data sets contain data from 55 able-bodied subjects, mean age 24 ± 2 years, measured in 85 measurement sessions. Measurement sessions consisted of trials containing sitting, standing, overground walking, stair ascent, stair descent, ramp ascent, ramp descent, walking on uneven terrain and walking in simulated confined spaces. Subjects were measured using eight inertial measurement units in combination with different types of sEMG. Recorded kinematics consisted of joint angles, sensor accelerations, angular velocity, orientation and virtual marker positions. sEMG was recorded using bipolar sEMG, multi-array sEMG or a combination of both. All data showed excellent correlation with other online available data sets. The data reported in this descriptor forms a solid basis for research into myoelectric pattern recognition, myoelectric control development and electromyography to be used in data-driven applications.

AB - This data descriptor describes the Roessingh Research & Development-MyLeg database for activity prediction (MyPredict), containing three data sets. These data sets contain data from 55 able-bodied subjects, mean age 24 ± 2 years, measured in 85 measurement sessions. Measurement sessions consisted of trials containing sitting, standing, overground walking, stair ascent, stair descent, ramp ascent, ramp descent, walking on uneven terrain and walking in simulated confined spaces. Subjects were measured using eight inertial measurement units in combination with different types of sEMG. Recorded kinematics consisted of joint angles, sensor accelerations, angular velocity, orientation and virtual marker positions. sEMG was recorded using bipolar sEMG, multi-array sEMG or a combination of both. All data showed excellent correlation with other online available data sets. The data reported in this descriptor forms a solid basis for research into myoelectric pattern recognition, myoelectric control development and electromyography to be used in data-driven applications.

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SN - 2052-4463

VL - 10

JO - Scientific Data

JF - Scientific Data

M1 - 461

ER -

Database of lower limb kinematics and electromyography during gait-related activities in able-bodied subjects

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Datasets

Roessingh Research & Development-MyLeg database for activity prediction (MyPredict)

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