运动机能减退综合征步态视频数据集
包含251个步行视频，用于训练和验证深度学习模型以诊断运动机能减退综合征。
基本信息
资源简介
运动机能减退综合征步态视频数据集
【数据集背景】
本数据集源站标题为《Internal validation by cross-validation.》。源站首次发布：2024-11-26。
【数据内容】
源站原始描述：Locomotive Syndrome (LS) is defined by decreased walking and standing abilities due to musculoskeletal issues. Early diagnosis is vital as LS can be reversed with appropriate intervention. Although diagnosing LS using standardized charts is straightforward, the labor-intensive and time-consuming nature of the process limits its widespread implementation. To address this, we introduced a Deep Learning (DL)-based computer vision model that employs OpenPose for pose estimation and MS-G3D for spatia…
【数据结构与技术规格】
文件数 1 个，合计 5.63 KB。Table 2.xls：application/vnd.ms-excel，5.63 KB。数据集 DOI：10.1371/journal.pdig.0000668.t002。
【主题与分类】
源站学科分类：Biotechnology、Biological Sciences not elsewhere classified、Information Systems not elsewhere classified、Mathematical Sciences not elsewhere classified、Science Policy。主题标签：standing abilities due、positive predictive value、overall accuracy rate、expediting treatment initiation、demonstrating high effectiveness、camera video captures、addressing key challenges、camera walking video、186 walking videos、model &# 8217。
【适用方向】
影像类数据可用于医学图像分析模型（检测、分割、分类）的训练与评测，也可用于标注质量与成像参数相关性的研究。
【获取与许可】
源站页面：https://plos.figshare.com/articles/dataset/Internal_validation_by_cross-validation_/27910818
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Kushioka, Junichi; Tada, Satoru; Takemura, Noriko; Fujimoto, Taku; Nagahara, Hajime; Onoe, Masahiko; et al. (2024). Internal validation by cross-validation.. PLOS Digital Health. Dataset
下载信息
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暂未开放当前数据集主要来源为 Kaggle 公开托管，完整影像包建议通过原始链接或 Kaggle API 获取。
使用方式
数据集获取（Figshare）
- 源站标题：Internal validation by cross-validation.
- DOI：10.1371/journal.pdig.0000668.t002
- 发布日期：2024-11-26
- 文件数：1 个
- 文件总体积：5.63 KB
- 源站页面：https://plos.figshare.com/articles/dataset/Internal_validation_by_cross-validation_/27910818
命令行下载
curl -L -o "Table 2.xls" "https://ndownloader.figshare.com/files/50819751"
文件清单
Table 2.xls（5.63 KB）
文件总体积 5.63 KB（源站 API 实测）。
数据说明
Locomotive Syndrome (LS) is defined by decreased walking and standing abilities due to musculoskeletal issues. Early diagnosis is vital as LS can be reversed with appropriate intervention. Although diagnosing LS using standardized charts is straightforward, the labor-intensive and time-consuming nature of the process limits its widespread implementation. To address this, we introduced a Deep Learning (DL)-based computer vision model that employs OpenPose for pose estimation and MS-G3D for spatial-temporal graph analysis. This model objectively assesses gait patterns through single-camera video captures, offering a novel and efficient method for LS prediction and analysis. Our model was trained and validated using a dataset of 186 walking videos, plus 65 additional videos for external validation. The model achieved an average sensitivity of 0.86, demonstrating high effectiveness in identifying individuals with LS. The model’s positive predictive value was 0.85, affirming its reliable LS detection, and it reached an overall accuracy rate of 0.77. External validation using an independent dataset confirmed strong generalizability with an Area Under the Curve of 0.75. Although the model accurately diagnosed LS cases, it was less precise in identifying non-LS cases. This study pioneers in diagnosing LS using computer vision technology for pose estimation. Our accessible, non-invasive model serves as a tool that can accurately diagnose the labor-intensive LS tests using only visual assessments, streamlining LS detection and expediting treatment initiation. This significantly improves patient outcomes and marks a crucial advancement in digital health, addressing key challenges in management and care of LS.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.1371/journal.pdig.0000668.t002（source: https://plos.figshare.com/articles/dataset/Internal_validation_by_cross-validation_/27910818）。
数据缺失？
依托陕西、四川两大基地，我们与超过十家三甲医院建立直接合作关系，覆盖合规授权、采集治理、专业标注、数据交付的全流程，为AI医疗团队提供即拿即用的高质量临床数据。
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