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下肢行走表面肌电、运动学和动力学数据集，用于运动意图识别。

Surface electromyogram, kinematic, and kinetic dataset of lower limb walking for movement intent recognition.

机构信息

CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Advanced Technology (SIAT), Chinese Academy of Sciences (CAS), and the SIAT Branch, Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, 518055, China.

Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, Guangdong, 518055, China.

出版信息

Sci Data. 2023 Jun 6;10(1):358. doi: 10.1038/s41597-023-02263-3.

DOI:10.1038/s41597-023-02263-3
PMID:37280249
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10244354/
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.

摘要

表面肌电图 (sEMG) 为解码肢体运动意图提供了丰富的运动信息，可作为智能人机协同系统 (IHMSS) 的控制输入。尽管人们对 IHMSS 的兴趣日益浓厚，但当前可用的公开数据集有限，难以满足研究人员不断增长的需求。本研究提出了一种新的下肢运动数据集（命名为 SIAT-LLMD），包含 sEMG、运动学和动力学数据以及 40 名健康人在 16 种运动中采集的相应标签。运动学和动力学数据使用运动捕捉系统和六维力平台采集，并使用 OpenSim 软件进行处理。sEMG 数据使用 9 个无线传感器记录在左侧肢体的大腿和小腿肌肉上。此外，SIAT-LLMD 提供了用于分类不同运动和不同步态阶段的标签。对数据集的分析验证了同步性和可重复性，并提供了有效的数据处理代码。该数据集可作为探索用于描述下肢运动的新算法和模型的新资源。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4035/10244354/53d107ccd2f5/41597_2023_2263_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4035/10244354/6d328fdc1cba/41597_2023_2263_Fig1_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4035/10244354/211a8c5894be/41597_2023_2263_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4035/10244354/1d2fcf04e02d/41597_2023_2263_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4035/10244354/e30bf911d5f6/41597_2023_2263_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4035/10244354/170c9d71c1ba/41597_2023_2263_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4035/10244354/10f93348f943/41597_2023_2263_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4035/10244354/53d107ccd2f5/41597_2023_2263_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4035/10244354/6d328fdc1cba/41597_2023_2263_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4035/10244354/0e9e24c71d00/41597_2023_2263_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4035/10244354/8692ffde7904/41597_2023_2263_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4035/10244354/211a8c5894be/41597_2023_2263_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4035/10244354/1d2fcf04e02d/41597_2023_2263_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4035/10244354/e30bf911d5f6/41597_2023_2263_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4035/10244354/170c9d71c1ba/41597_2023_2263_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4035/10244354/10f93348f943/41597_2023_2263_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4035/10244354/53d107ccd2f5/41597_2023_2263_Fig9_HTML.jpg

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