Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

A Multi-Modular Human-AI Workflow for LLM-Assisted Thematic Analysis: Application to COPD Telerehabilitation Interviews.

Studies in health technology and informatics·2026
Same author

Association of Remote Patient Monitoring with Care Utilization in Patients with Chronic Cardiopulmonary Conditions.

Studies in health technology and informatics·2026
Same author

Machine Learning Approaches for Mortality Prediction in ARDS.

Studies in health technology and informatics·2026
Same author

Estimating Mosaic Loss of the Y Chromosome in Male Bladder Cancer Participants Using "All of Us" Data.

Studies in health technology and informatics·2026
Same author

Early Prediction of Delirium in ICU Patients Using Machine Learning Analysis of Admission Data.

Studies in health technology and informatics·2026
Same author

Multimodule Human-Artificial Intelligence Collaboration Pipeline for Large Language Model-Assisted Thematic Analysis Across Digital Health Interview Studies: Comparative Evaluation Study.

JMIR medical informatics·2026

相关实验视频

Updated: Jul 5, 2025

Assessing the Accuracy of Fitness Smartwatch Data for Cardiovascular and Physical Activity Monitoring: A Validation Study in Digital Health
05:51

Assessing the Accuracy of Fitness Smartwatch Data for Cardiovascular and Physical Activity Monitoring: A Validation Study in Digital Health

Published on: February 21, 2025

461

机器学习方法用于运动运动水平分类,使用可穿戴生理监视器数据.

Aref Smiley1, Te-Yi Tsai1, Ihor Havrylchuk1

  • 1Center for Biomedical and Population Health Informatics, Icahn School of Medicine at Mount Sinai, New York, USA.

Studies in health technology and informatics
|January 25, 2024
PubMed
概括
此摘要是机器生成的。

这项研究预测了使用心率变化 (HRV) 来从心电图信号的有氧运动炼水平. 一个支持矢量机器模型实现了82%的准确性,使实时监控炼强度成为可能.

关键词:
机器学习是机器学习.有氧运动是有氧运动.劳动力水平 劳动力水平

更多相关视频

A Novel Digital Platform for a Monitored Home-based Cardiac Rehabilitation Program
04:24

A Novel Digital Platform for a Monitored Home-based Cardiac Rehabilitation Program

Published on: April 19, 2019

11.6K
Evaluation of Commercial-Off-The-Shelf Wrist Wearables to Estimate Stress on Students
12:51

Evaluation of Commercial-Off-The-Shelf Wrist Wearables to Estimate Stress on Students

Published on: June 16, 2018

7.5K

相关实验视频

Last Updated: Jul 5, 2025

Assessing the Accuracy of Fitness Smartwatch Data for Cardiovascular and Physical Activity Monitoring: A Validation Study in Digital Health
05:51

Assessing the Accuracy of Fitness Smartwatch Data for Cardiovascular and Physical Activity Monitoring: A Validation Study in Digital Health

Published on: February 21, 2025

461
A Novel Digital Platform for a Monitored Home-based Cardiac Rehabilitation Program
04:24

A Novel Digital Platform for a Monitored Home-based Cardiac Rehabilitation Program

Published on: April 19, 2019

11.6K
Evaluation of Commercial-Off-The-Shelf Wrist Wearables to Estimate Stress on Students
12:51

Evaluation of Commercial-Off-The-Shelf Wrist Wearables to Estimate Stress on Students

Published on: June 16, 2018

7.5K

科学领域:

  • 运动生理学 运动生理学
  • 生物医学工程 生物医学工程
  • 数据科学数据科学数据科学

背景情况:

  • 监测有氧运动的运动量对于优化训练和防止过度炼至关重要.
  • 心率变化 (HRV) 是运动期间自主神经系统活动的非侵入性生理标志物.
  • 对炼水平的实时反可以提高炼坚持和表现.

研究的目的:

  • 开发实时有氧运动炼水平的预测模型.
  • 识别关键的心率变化特征,用于分类运动状态.
  • 评估机器学习模型在预测炼方面的性能.

主要方法:

  • 在16分钟的自行车运动中记录了心电图信号.
  • 收集感知劳动力 (RPE) 评级,将运动分钟标记为"高"或"低"劳动力.
  • 时间和频率域HRV特征被提取并使用mRMR算法进行排名.
  • 一个支持向量机分类器被训练和评估.

主要成果:

  • 最小冗余最大相关性 (mRMR) 算法确定了前十个预测HRV特征.
  • 支持矢量机器模型实现了最高的分类准确性.
  • 在预测高与低的运动水平方面,获得了82%的F1得分.

结论:

  • 从心电图信号中获得的HRV特征可以有效地预测空气运动的实时炼水平.
  • 机器学习模型,特别是支持向量机器,非常适合开发这种预测系统.
  • 这种方法具有个性化炼指导和绩效监测的潜力.