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

• Published in: Scientific data Vol. 10; no. 1; pp. 461 - 10
• Main Authors: Schulte, Robert V., Prinsen, Erik C., Schaake, Leendert, Paassen, Robert P. G., Zondag, Marijke, van Staveren, Eline S., Poel, Mannes, Buurke, Jaap H.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 14.07.2023; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114/1305; 639/166/985; Adult; Biomechanical Phenomena; Data Descriptor; Datasets; Deep learning; Electrodes; Electromyography; Gait; Humanities and Social Sciences; Humans; Kinematics; Lower Extremity; multidisciplinary; Pattern recognition; R&D; Research & development; Science; Science (multidisciplinary); Sensors; Software; Walking; Young Adult
• ISSN: 2052-4463; 2052-4463
• DOI: 10.1038/s41597-023-02341-6

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.