帕金森姿势估计数据集
包含帕金森病评估视频中提取的运动轨迹、置信度值及UDysRS/UPDRS/CAPSIT评分的数据集。
基本信息
资源简介
帕金森姿势估计数据集
【数据集背景】
本数据集源站标题为《limi44/Parkinson-s-Pose-Estimation-Dataset》。源站首次发布：2017-07-07。
【数据内容】
源站原始描述：Demo notebook for Parkinson''s Pose Estimation Dataset # Parkinson''s Pose Estimation Dataset This notebook describes the dataset accompanying the paper: ''Vision-Based Assessment of Parkinsonism and Levodopa-Induced Dyskinesia with Deep Learning Pose Estimation'' - Li, Mestre, Fox, Taati (2018). The dataset is available here and is released under the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit The data includes all movement trajectories extracted fr…
【适用方向】
多模态数据适合跨模态对齐、融合建模与联合表征学习方向的研究。
【获取与许可】
源站页面：https://github.com/limi44/Parkinson-s-Pose-Estimation-Dataset
【使用注意】
· 源站最后修改于 2018-11-10，引用时请注意版本时效。
下载信息
注册下载
需要注册 Kaggle 账号并登录后下载，适合需要跟踪下载记录和使用 API 的用户。
暂未开放公开下载
无需注册即可直接获取公开样本或文档，适合快速预览和评估数据集质量。
免登录有偿下载
公开数据集受托下载与技术交付服务。
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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
git clone https://github.com/limi44/Parkinson-s-Pose-Estimation-Dataset.git
curl -L -o repo.zip https://github.com/limi44/Parkinson-s-Pose-Estimation-Dataset/archive/refs/heads/master.zip
unzip repo.zip
源站 README 摘录（使用方式）
Parkinson’'s Pose Estimation Dataset
This notebook describes the dataset accompanying the paper: <a href=“https://jneuroengrehab.biomedcentral.com/articles/10.1186/s12984-018-0446-z”>‘‘Vision-Based Assessment of Parkinsonism and Levodopa-Induced Dyskinesia with Deep Learning Pose Estimation’’</a> - Li, Mestre, Fox, Taati (2018).
The dataset is available <a href=“http://individual.utoronto.ca/BabakTaati/Data/PD/UDysRS_UPDRS_Export.zip”>here</a> and is released under the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
The data includes all movement trajectories extracted from the videos of Parkinson’'s assessments using Convolutional Pose Machines (CPM) (https://arxiv.org/abs/1602.00134), as well as the confidence values from CPM. The dataset also includes ground truth ratings of parkinsonism and dyskinesia severity using the UDysRS, UPDRS, and CAPSIT.
If you use this dataset in your work, please cite the following reference:
<b> M.H. Li, T.A. Mestre, S.H. Fox, and B. Taati, Vision-based assessment of parkinsonism and levodopa-induced dyskinesia with pose estimation, Journal of NeuroEngineering and Rehabilitation, vol. 15, no. 1, p. 97, Nov. 2018. doi:10.1186/s12984-018-0446-z</b>
You may also find the following paper useful. In this paper, we evaluated the responsiveness of features to clinically relevant changes in dyskinesia severity:
<b> M.H. Li, T.A. Mestre, S.H. Fox, B. Taati, Automated assessment of levodopa-induced dyskinesia: Evaluating the responsiveness of video-based features, Parkinsonism & Related Disorders. (2018). doi:10.1016/j.parkreldis.2018.04.036.</b>
数据加载示例（表格/文本类）
import pandas as pd, glob, os
files = (glob.glob(os.path.join(path, "**", "*.csv"), recursive=True)
+ glob.glob(os.path.join(path, "**", "*.tsv"), recursive=True)
+ glob.glob(os.path.join(path, "**", "*.xlsx"), recursive=True))
print("数据文件:", files)
df = pd.read_csv(files[0])
print(df.shape); print(df.columns.tolist()); print(df.head(3))
数据缺失？
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