使用可穿戴传感器评估上肢任务中运动障碍的范围综述表2

数据集获取（Figshare）

• 源站标题：Table2_Assessment of movement disorders using wearable sensors during upper limb tasks: A scoping review.docx
• DOI：10.3389/frobt.2022.1068413.s003
• 发布日期：2023-01-09
• 文件数：1 个
• 文件总体积：108.04 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table2_Assessment_of_movement_disorders_using_wearable_sensors_during_upper_limb_tasks_A_scoping_review_docx/21838032

命令行下载

curl -L -o "Table2_Assessment of movement disorders using wearable sensors during upper limb tasks: A scoping review.docx" "https://ndownloader.figshare.com/files/38758272"

文件清单

• Table2_Assessment of movement disorders using wearable sensors during upper limb tasks: A scoping review.docx（108.04 KB）

文件总体积 108.04 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

Background: Studies aiming to objectively quantify movement disorders during upper limb tasks using wearable sensors have recently increased, but there is a wide variety in described measurement and analyzing methods, hampering standardization of methods in research and clinics. Therefore, the primary objective of this review was to provide an overview of sensor set-up and type, included tasks, sensor features and methods used to quantify movement disorders during upper limb tasks in multiple pathological populations. The secondary objective was to identify the most sensitive sensor features for the detection and quantification of movement disorders on the one hand and to describe the clinical application of the proposed methods on the other hand.
Methods: A literature search using Scopus, Web of Science, and PubMed was performed. Articles needed to meet following criteria: 1) participants were adults/children with a neurological disease, 2) (at least) one sensor was placed on the upper limb for evaluation of movement disorders during upper limb tasks, 3) comparisons between: groups with/without movement disorders, sensor features before/after intervention, or sensor features with a clinical scale for assessment of the movement disorder. 4) Outcome measures included sensor features from acceleration/angular velocity signals.
Results: A total of 101 articles were included, of which 56 researched Parkinson’s Disease. Wrist(s), hand(s) and index finger(s) were the most popular sensor locations. Most frequent tasks were: finger tapping, wrist pro/supination, keeping the arms extended in front of the body and finger-to-nose. Most frequently calculated sensor features were mean, standard deviation, root-mean-square, ranges, skewness, kurtosis/entropy of acceleration and/or angular velocity, in combination with dominant frequencies/power of acceleration signals. Examples of clinical applications were automatization of a clinical scale or discrimination between a patient/control group or different patient groups.
Conclusion: Current overview can support clinicians and researchers in selecting the most sensitive pathology-dependent sensor features and methodologies for d

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/frobt.2022.1068413.s003（source: https://frontiersin.figshare.com/articles/dataset/Table2_Assessment_of_movement_disorders_using_wearable_sensors_during_upper_limb_tasks_A_scoping_review_docx/21838032）。