智能助行器对卒中患者适应性的sEMG与加速度计信号研究

数据集获取（Figshare）

• 源站标题：Adaptation of a smart walker for stroke individuals: a study on sEMG and accelerometer signals
• DOI：10.6084/m9.figshare.5666758.v1
• 发布日期：2017-12-05
• 文件数：6 个
• 文件总体积：139.27 KB
• 源站页面：https://scielo.figshare.com/articles/dataset/Adaptation_of_a_smart_walker_for_stroke_individuals_a_study_on_sEMG_and_accelerometer_signals/5666758

命令行下载

curl -L -o "2446-4740-reng-2446-474001717-gf01.jpg" "https://ndownloader.figshare.com/files/9892150"
curl -L -o "2446-4740-reng-2446-474001717-gf02.jpg" "https://ndownloader.figshare.com/files/9892153"
curl -L -o "Table_1.xls" "https://ndownloader.figshare.com/files/9892156"
curl -L -o "Table_2.xls" "https://ndownloader.figshare.com/files/9892159"
curl -L -o "Table_3.xls" "https://ndownloader.figshare.com/files/9892162"
curl -L -o "Table_4.xls" "https://ndownloader.figshare.com/files/9892165"

文件清单

• 2446-4740-reng-2446-474001717-gf01.jpg（44.43 KB）
• 2446-4740-reng-2446-474001717-gf02.jpg（72.31 KB）
• Table_1.xls（5.63 KB）
• Table_2.xls（5.63 KB）
• Table_3.xls（5.63 KB）
• Table_4.xls（5.63 KB）

文件总体积 139.27 KB（源站 API 实测）。

数据加载示例（图像类）

from PIL import Image
import glob, os

files = (glob.glob(os.path.join(path, "**", "*.png"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.jpg"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.tif"), recursive=True))
print("图像文件数:", len(files))
img = Image.open(files[0]); print("尺寸/模式:", img.size, img.mode)

# torchvision Dataset 方式:
# from torchvision import datasets
# ds = datasets.ImageFolder(path)  # 要求 子目录=类别

目录组织与标注格式以源站说明和下载后实际文件为准。

数据说明

Abstract Introduction: Stroke is a leading cause of neuromuscular system damages, and researchers have been studying and developing robotic devices to assist affected people. Depending on the damage extension, the gait of these people can be impaired, making devices, such as smart walkers, useful for rehabilitation. The goal of this work is to analyze changes in muscle patterns on the paretic limb during free and walker-assisted gaits in stroke individuals, through accelerometry and surface electromyography (sEMG). Methods The analyzed muscles were vastus medialis, biceps femoris, tibialis anterior and gastrocnemius medialis. The volunteers walked three times on a straight path in free gait and, further, three times again, but now using the smart walker, to help them with the movements. Then, the data from gait pattern and muscle signals collected by sEMG and accelerometers were analyzed and statistical analyses were applied. Results The accelerometry allowed gait phase identification (stance and swing), and sEMG provided information about muscle pattern variations, which were detected in vastus medialis (onset and offset; p = 0.022) and biceps femoris (offset; p = 0.025). Additionally, comparisons between free and walker-assisted gaits showed significant reduction in speed (from 0.45 to 0.30 m/s; p = 0.021) and longer stance phase (from 54.75 to 60.34%; p = 0.008). Conclusions Variations in muscle patterns were detected in vastus medialis and biceps femoris during the experiments, besides user speed reduction and longer stance phase when the walker-assisted gait is compared with the free gait.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.6084/m9.figshare.5666758.v1（source: https://scielo.figshare.com/articles/dataset/Adaptation_of_a_smart_walker_for_stroke_individuals_a_study_on_sEMG_and_accelerometer_signals/5666758）。