桡神经、尺神经、正中神经损伤患者异常手势自动检测数据集（表1）

数据集获取（Figshare）

• 源站标题：Table_1_Automatic detection of abnormal hand gestures in patients with radial, ulnar, or median nerve injury using hand pose estimation.XLSX
• DOI：10.3389/fneur.2022.1052505.s001
• 发布日期：2022-12-07
• 文件数：1 个
• 文件总体积：11.92 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_1_Automatic_detection_of_abnormal_hand_gestures_in_patients_with_radial_ulnar_or_median_nerve_injury_using_hand_pose_estimation_XLSX/21685937

命令行下载

curl -L -o "Table_1_Automatic detection of abnormal hand gestures in patients with radial, ulnar, or median nerve injury using hand pose estimation.XLSX" "https://ndownloader.figshare.com/files/38458724"

文件清单

• Table_1_Automatic detection of abnormal hand gestures in patients with radial, ulnar, or median nerve injury using hand pose estimation.XLSX（11.92 KB）

文件总体积 11.92 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

BackgroundRadial, ulnar, or median nerve injuries are common peripheral nerve injuries. They usually present specific abnormal signs on the hands as evidence for hand surgeons to diagnose. However, without specialized knowledge, it is difficult for primary healthcare providers to recognize the clinical meaning and the potential nerve injuries through the abnormalities, often leading to misdiagnosis. Developing technologies for automatically detecting abnormal hand gestures would assist general medical service practitioners with an early diagnosis and treatment.
MethodsBased on expert experience, we selected three hand gestures with predetermined features and rules as three independent binary classification tasks for abnormal gesture detection. Images from patients with unilateral radial, ulnar, or median nerve injuries and healthy volunteers were obtained using a smartphone. The landmark coordinates were extracted using Google MediaPipe Hands to calculate the features. The receiver operating characteristic curve was employed for feature selection. We compared the performance of rule-based models with logistic regression, support vector machine and of random forest machine learning models by evaluating the accuracy, sensitivity, and specificity.
ResultsThe study included 1,344 images, twenty-two patients, and thirty-four volunteers. In rule-based models, eight features were finally selected. The accuracy, sensitivity, and specificity were (1) 98.2, 91.7, and 99.0% for radial nerve injury detection; (2) 97.3, 83.3, and 99.0% for ulnar nerve injury detection; and (3) 96.4, 87.5, and 97.1% for median nerve injury detection, respectively. All machine learning models had accuracy above 95% and sensitivity ranging from 37.5 to 100%.
ConclusionOur study provides a helpful tool for detecting abnormal gestures in radial, ulnar, or median nerve injuries with satisfying accuracy, sensitivity, and specificity. It confirms that hand pose estimation could automatically analyze and detect the abnormalities from images of these patients. It has the potential to be a simple and convenient screening method for primary healthcare and telemedicine application.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fneur.2022.1052505.s001（source: https://frontiersin.figshare.com/articles/dataset/Table_1_Automatic_detection_of_abnormal_hand_gestures_in_patients_with_radial_ulnar_or_median_nerve_injury_using_hand_pose_estimation_XLSX/21685937）。