可穿戴传感器客观测量帕金森病步态参数

数据集获取（Figshare）

• 源站标题：Wearable sensors objectively measure gait parameters in Parkinson’s disease
• DOI：10.1371/journal.pone.0183989
• 发布日期：2017-10-11
• 文件数：5 个
• 文件总体积：5.76 MB
• 源站页面：https://plos.figshare.com/articles/dataset/Wearable_sensors_objectively_measure_gait_parameters_in_Parkinson_s_disease/5489173

命令行下载

curl -L -o "S1 Fig.tif" "https://ndownloader.figshare.com/files/9498058"
curl -L -o "S2 Fig.tiff" "https://ndownloader.figshare.com/files/9498076"
curl -L -o "S1 Table.docx" "https://ndownloader.figshare.com/files/9498079"
curl -L -o "S2 Table.docx" "https://ndownloader.figshare.com/files/9498085"
curl -L -o "S3 Table.docx" "https://ndownloader.figshare.com/files/9498103"

文件清单

• S1 Fig.tif（2.55 MB）
• S2 Fig.tiff（3.00 MB）
• S1 Table.docx（83.49 KB）
• S2 Table.docx（76.07 KB）
• S3 Table.docx（58.06 KB）

文件总体积 5.76 MB（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

Distinct gait characteristics like short steps and shuffling gait are prototypical signs commonly observed in Parkinson’s disease. Routinely assessed by observation through clinicians, gait is rated as part of categorical clinical scores. There is an increasing need to provide quantitative measurements of gait, e.g. to provide detailed information about disease progression. Recently, we developed a wearable sensor-based gait analysis system as diagnostic tool that objectively assesses gait parameter in Parkinson’s disease without the need of having a specialized gait laboratory. This system consists of inertial sensor units attached laterally to both shoes. The computed target of measures are spatiotemporal gait parameters including stride length and time, stance phase time, heel-strike and toe-off angle, toe clearance, and inter-stride variation from gait sequences. To translate this prototype into medical care, we conducted a cross-sectional study including 190 Parkinson’s disease patients and 101 age-matched controls and measured gait characteristics during a 4x10 meter walk at the subjects’ preferred speed. To determine intraindividual changes in gait, we monitored the gait characteristics of 63 patients longitudinally. Cross-sectional analysis revealed distinct spatiotemporal gait parameter differences reflecting typical Parkinson’s disease gait characteristics including short steps, shuffling gait, and postural instability specific for different disease stages and levels of motor impairment. The longitudinal analysis revealed that gait parameters were sensitive to changes by mirroring the progressive nature of Parkinson’s disease and corresponded to physician ratings. Taken together, we successfully show that wearable sensor-based gait analysis reaches clinical applicability providing a high biomechanical resolution for gait impairment in Parkinson’s disease. These data demonstrate the feasibility and applicability of objective wearable sensor-based gait measurement in Parkinson’s disease reaching high technological readiness levels for both, large scale clinical studies and individual patient care.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pone.0183989（source: https://plos.figshare.com/articles/dataset/Wearable_sensors_objectively_measure_gait_parameters_in_Parkinson_s_disease/5489173）。