Surface Electromyogram, Kinematic, and Kinetic Dataset of Lower Limb Walking for Movement Intent Recognition

Journal article

Wei, W., Tan, F., Zhang, H., Mao, H., Fu, M., Samuel, O.W. and Li, G. 2023. Surface Electromyogram, Kinematic, and Kinetic Dataset of Lower Limb Walking for Movement Intent Recognition. Nature Scientific Data. 10 (358), pp. 1-16. https://doi.org/10.1038/s41597-023-02263-3
AuthorsWei, W., Tan, F., Zhang, H., Mao, H., Fu, M., Samuel, O.W. and Li, G.
Abstract

Surface electromyogram (sEMG) offers a rich set of motor information for decoding limb motion intention that serves as a control input to Intelligent human-machine synergy systems (IHMSS). Despite growing interest in IHMSS, the current publicly available datasets are limited and can hardly meet the growing demands of researchers. This study presents a novel lower limb motion dataset (designated as SIAT-LLMD), comprising sEMG, kinematic, and kinetic data with corresponding labels acquired from 40 healthy humans during 16 movements. The kinematic and kinetic data were collected using a motion capture system and six-dimensional force platforms and processed using OpenSim software. The sEMG data were recorded using nine wireless sensors placed on the subjects' thigh and calf muscles on the left limb. Besides, SIAT-LLMD provides labels to classify the different movements and different gait phases. Analysis of the dataset verified the synchronization and reproducibility, and codes for effective data processing are provided. The proposed dataset can serve as a new resource for exploring novel algorithms and models for characterizing lower limb movements.

KeywordsLower Limb Motion Intent Recognition; Surface Electromyogram, Kinematic, and Kinetic Dataset; Intelligent Human-Machine Synergy Systems
Year2023
JournalNature Scientific Data
Journal citation10 (358), pp. 1-16
PublisherNature Research
ISSN2052-4463
Digital Object Identifier (DOI)https://doi.org/10.1038/s41597-023-02263-3
Web address (URL)https://www.nature.com/articles/s41597-023-02263-3
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Output statusPublished
Publication dates
Online06 Jun 2023
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Accepted23 May 2023
Deposited22 Jun 2023
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