Auto-Gait共济失调步态视频数据集
Auto-Gait共济失调步态视频数据集 是真实存在且可公开访问或申请访问的数据集，包含视频数据、标签、临床变量或随访信息。它可用于共济失调领域的步态异常检测、SARA严重度评估、影像分类、遗传样本分析和临床表型抽取，支持模型训练、基准复现和跨队列验证。
基本信息
资源简介
Auto-Gait共济失调步态视频数据集 提供 SCA/共济失调受试者 SARA 步态任务视频衍生姿态关键点和严重度评分访问说明，适合共济失调相关的步态异常检测、SARA严重度评估、影像分类、遗传样本分析和临床表型抽取。
下载信息
注册下载
Tips: 该数据集需要在对应的数据源网站注册通过后，才能进行数据下载，注册有对应要求，或者需要收费。
暂未开放公开下载
Tips: 该数据集属于公开下载，应该可以免费公开下载。
免登录有偿下载
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提供高速下载与技术交付服务（收技术服务费，非数据销售）
暂未开放千方医数集，医疗数据集部分，是为社区服务的公开医疗数据集搜索引擎，并不存储或者下载原始的任何数据。 如果您有其他医疗数据需求，可以和客服联系，或者下工单。我们有强大的三甲医疗机构帮助您提供个性化的医疗数据定制、采集、标注服务。
使用方式
数据集获取
git clone https://github.com/ROC-HCI/Automated-Ataxia-Gait.git
curl -L -o repo.zip https://github.com/ROC-HCI/Automated-Ataxia-Gait/archive/refs/heads/main.zip
unzip repo.zip
源站 README 摘录（使用方式）
This repository contains code and an anonymized version of the dataset used in the paper, Auto-Gait: Automatic Ataxia Risk Assessment with Computer Vision from Gait Task Videos published at Proceedings of ACM on Interactive, Mobile, Wearable, and Ubiquitous Computing (IMWUT), 2023.
Anonymized dataset: https://rochester.box.com/v/AtaxiaDataset
Warning: In order to make the public release, the participant faces and the background had to be blurred. Which might cause the object detection and pose estimation models to fail.
If you use this dataset, please provide attribution to READISCA and NIH grant number U01NS104326.
Ataxia is a neurodegenerative disease that surfaces as difficulty with motor control, such as walking imbalance. Many patients with Ataxia do not have easy access to neurologists – especially those living in remote localities and developing/ underdeveloped countries. In a multi-year collaboration effort with READISCA, we collected a video dataset of ataxia diagnosed and control subjects from 11 clinics located in 8 different states across the United States. The dataset contains 150 (anonymized version of the 155 videos used in the original study) 6 seconds long videos from 89 unique study participants (24 control, 65 diagnosed) performing gait task.
Furthermore, we developed a computer vision and machine learning pipeline to identify, track, and separate participants from complex surroundings and predict their risk and severity of Spinocerebellar Ataxia. Ataxia risk-prediction model achieves 83.06% accuracy and an 80.23% F1 score. Ataxia severity-assessment model achieves MAE score of 0.6225 and a Pearson’'s correlation coefficient score of 0.7268. Our model performs competitively while tested on clinics completely unseen during training. Our feature importance analysis shows the model automatically picks up traits that are consistent with established clinical knowledge.
Limitations:
- 5 of the videos were removed during the anonymization process for the public release. Hence the results using this dataset might vary slightly from the original study.
- #146 is a video of the study subject attempting tandem walking instead of regular walk.
- #144, the subject is wearing shoes, which is a protocol violation, but pra
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。
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