可穿戴康复腕带用于桡骨远端骨折数据集

数据集获取（Figshare）

• 源站标题：Table_1_Wearable rehabilitation wristband for distal radius fractures.XLS
• DOI：10.3389/fnins.2023.1238176.s003
• 发布日期：2023-09-14
• 文件数：1 个
• 文件总体积：2.67 MB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_1_Wearable_rehabilitation_wristband_for_distal_radius_fractures_XLS/24136632

命令行下载

curl -L -o "Table_1_Wearable rehabilitation wristband for distal radius fractures.XLS" "https://ndownloader.figshare.com/files/42344817"

文件清单

• Table_1_Wearable rehabilitation wristband for distal radius fractures.XLS（2.67 MB）

文件总体积 2.67 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

BackgroundDistal radius fractures are a common type of fracture. For patients treated with closed reduction with splinting, a period of rehabilitation is still required after the removal of the splint. However, there is a general lack of attention and low compliance to rehabilitation training during this period, so it is necessary to build a rehabilitation training monitoring system to improve the efficiency of patients’ rehabilitation.
MethodsA wearable rehabilitation training wristband was proposed, which could be used in the patient’s daily rehabilitation training scenario and could recognize four common wrist rehabilitation actions in real-time by using three thin film pressure sensors to detect the pressure change curve at three points on the wrist. An algorithmic framework for classifying rehabilitation training actions was proposed. In our framework, an action pre-detection strategy was designed to exclude false detections caused by switching initial gestures during rehabilitation training and wait for the arrival of the complete signal. To classify the action signals into four categories, firstly an autoencoder was used to downscale the original signal. Six SVMs were then used for evaluation and voting, and the final action with the highest number of votes would be used as the prediction result.
ResultsExperimental results showed that the proposed algorithmic framework achieved an average recognition accuracy of 89.62%, an average recognition recall of 88.93%, and an f1 score of 89.27% on the four rehabilitation training actions.
ConclusionThe developed device has the advantages of being small size and easy to wear, which can quickly and accurately identify and classify four common rehabilitation training actions. It can easily be combined with peripheral devices and technologies (e.g., cell phones, computers, Internet) to build different rehabilitation training scenarios, making it worthwhile to use and promote in clinical settings.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fnins.2023.1238176.s003（source: https://frontiersin.figshare.com/articles/dataset/Table_1_Wearable_rehabilitation_wristband_for_distal_radius_fractures_XLS/24136632）。