智能手表脉搏丧失检测数据集

提供20人PPG和IMU信号的多模态数据集,用于检测脉搏丧失并区分智能手表未使用情况。

NIAID Data Ecosystem
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时间序列, 多模态prevent false alarmsextracting several featuresevaluated using leavepresented framework consistspresented framework achievesfalse detection ratepulse cases usingcommercially available smartwatchpulse </ pdetecting pulse losspulse casesdetecting loss
Smartwatch Pulse Loss Detection Dataset

基本信息

模态
时间序列, 多模态
大小
0.000005632 GB
许可
CC BY 4.0
创建/更新时间
2026-10-08
版本
v1

资源简介

智能手表脉搏丧失检测数据集

【一句话概览】

本数据集是 figshare 公开的多模态数据集,收录 1 个文件、合计 5.63 KB,主题涵盖 prevent false alarms、extracting several features、evaluated using leave、presented framework consists、presented framework achieves,可用于跨模态对齐与融合建模研究,CC BY 4.0 许可开放获取。

【数据集背景】

本数据集源站标题为《Summary of duration and segment count by case.》。源站首次发布:2026-02-27。

【数据内容】

源站原始描述:We present a framework and a novel dataset for detecting loss of pulse cases using a smartwatch. Our methodology for gathering data involves inducing controlled pulse loss in the forearm within a clinical setting under medical supervision. To the best of our knowledge, this is the first multimodal dataset comprising photoplethysmogram (PPG) signals, inertial measurements (IMUs) that can be obtained from a commercially available smartwatch, and continuous recordings as ground truth, collected fro…

【数据结构与技术规格】

文件数 1 个,合计 5.63 KB。Table 1.xls:application/vnd.ms-excel,5.63 KB。数据集 DOI:10.1371/journal.pone.0342064.t001。

【主题与分类】

源站学科分类:Space Science、Cell Biology、Genetics、Biological Sciences not elsewhere classified、Cancer。主题标签:prevent false alarms、extracting several features、evaluated using leave、presented framework consists、presented framework achieves、false detection rate、pulse cases using、commercially available smartwatch、detecting pulse loss、pulse cases。

【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://plos.figshare.com/articles/dataset/_p_Summary_of_duration_and_segment_count_by_case_p_/31432466

使用许可:CC BY 4.0(https://creativecommons.org/licenses/by/4.0/)。

规范引用:Demirel, Berken Utku; Akay, Galip Utku; Streli, Paul; Ozturkmen, Hatice; Holz, Christian (2026). Summary of duration and segment count by case.. PLOS ONE. Dataset

官方服务

专家保障全链路合规授权与标注交付

陕川双基地 × 超十家三甲医院直连

从合规授权、采集治理、专业标注到数据集交付,我们提供全链路闭环服务,让高质量临床数据即拿即用。

⚡️ 需要原始数据或标注支持?联系我们获取定制方案。

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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。

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

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)

命令行下载

curl -L -o "Table 1.xls" "https://ndownloader.figshare.com/files/62243696"

文件清单

  • Table 1.xls(5.63 KB)

文件总体积 5.63 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

We present a framework and a novel dataset for detecting loss of pulse cases using a smartwatch. Our methodology for gathering data involves inducing controlled pulse loss in the forearm within a clinical setting under medical supervision. To the best of our knowledge, this is the first multimodal dataset comprising photoplethysmogram (PPG) signals, inertial measurements (IMUs) that can be obtained from a commercially available smartwatch, and continuous recordings as ground truth, collected from 20 individuals. This dataset serves as a valuable resource for the research community to advance and verify techniques for detecting pulse loss and differentiate between the non-usage of smartwatches to prevent false alarms. Our presented framework consists of extracting several features from signals and employing machine learning models designed to differentiate between the emergency loss of pulse cases with the non-usage of smartwatches. The presented framework achieves a detection accuracy of up to 97.1%, with a false detection rate of 1.0 in an hour when evaluated using leave-one-subject-out cross-validation.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pone.0342064.t001(source: https://plos.figshare.com/articles/dataset/_p_Summary_of_duration_and_segment_count_by_case_p_/31432466)。

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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