Jove
Visualize
联系我们

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

Medium- and Long-Term Effectiveness of Custom Insoles for Cavus Foot: A Surface Electromyography Study.

Journal of functional morphology and kinesiology·2025
Same author

Short-Term Foot and Postural Adaptations During an Industrial Workday: A Workplace-Based Biomechanical Assessment.

Journal of functional morphology and kinesiology·2025
Same author

Gait-Based Screening for Cognitive Impairment in Older Adults: A Fast and Objective Approach.

Healthcare (Basel, Switzerland)·2025
Same author

A Diagnostic and Performance System for Soccer: Technical Design and Development.

Sports (Basel, Switzerland)·2025
Same author

Digitalization of an Industrial Process for Bearing Production.

Sensors (Basel, Switzerland)·2024
Same author

BodyFlow: An Open-Source Library for Multimodal Human Activity Recognition.

Sensors (Basel, Switzerland)·2024
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

相关实验视频

Updated: Jun 3, 2025

Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.6K

基于步行的人工智能模型用于检测使用传感器融合的萨尔科佩尼亚和认知衰退.

Rocío Aznar-Gimeno1, Jose Luis Perez-Lasierra2,3, Pablo Pérez-Lázaro1

  • 1Department of Big Data and Cognitive Systems, Instituto Tecnológico de Aragón (ITA), María de Luna 7-8, 50018 Zaragoza, Spain.

Diagnostics (Basel, Switzerland)
|January 8, 2025
PubMed
概括

使用步态分析的人工智能可以检测老年人肉症和认知衰退 (CD). 这种人工智能方法结合了传感器和计算机视觉数据,用于早期疾病查和干预.

关键词:
人工智能的人工智能是人工智能.人类姿势估计估计惯性测量单位是一种惯性测量单位.机器学习是机器学习.肌肉骨系统疾病 肌肉骨疾病年龄较大的成年人.可穿戴式传感器传感器

更多相关视频

Home-Based Monitor for Gait and Activity Analysis
07:24

Home-Based Monitor for Gait and Activity Analysis

Published on: August 8, 2019

6.7K
Exergaming in Older People Living with HIV Improves Balance, Mobility and Ameliorates Some Aspects of Frailty
07:27

Exergaming in Older People Living with HIV Improves Balance, Mobility and Ameliorates Some Aspects of Frailty

Published on: October 6, 2016

10.2K

相关实验视频

Last Updated: Jun 3, 2025

Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.6K
Home-Based Monitor for Gait and Activity Analysis
07:24

Home-Based Monitor for Gait and Activity Analysis

Published on: August 8, 2019

6.7K
Exergaming in Older People Living with HIV Improves Balance, Mobility and Ameliorates Some Aspects of Frailty
07:27

Exergaming in Older People Living with HIV Improves Balance, Mobility and Ameliorates Some Aspects of Frailty

Published on: October 6, 2016

10.2K

科学领域:

  • 老年学是一门学科.
  • 生物医学工程 生物医学工程
  • 人工智能的人工智能

背景情况:

  • 麻症和认知衰退 (CD) 显著影响老龄化人口的生活质量.
  • 早期检测受到传统面对面查方法的局限性阻碍.
  • 开发可访问,定期查工具对于及时干预至关重要.

研究的目的:

  • 开发人工智能算法,使用步态分析检测肉症和CD.
  • 整合传感器和计算机视觉 (CV) 数据,以提高预测准确度.
  • 探索多模式步态分析在疾病早期检测方面的潜力.

主要方法:

  • 一项涉及42名老年人 (≥60岁) 的横截面病例控制研究.
  • 使用足部/腰部传感器和在正常行走过程中使用心血管数据评估的步行模式.
  • 机器学习模型开发使用提取的步态变量来预测肉症和CD.

主要成果:

  • 人工智能模型对CD和sarcopenia都有很高的预测准确度.
  • 最好的CD模型获得了0.914的F1得分 (95%的灵敏度,92%的特异性).
  • 综合的传感器和CV模型对皮症的F1得分为0.748 (100%的灵敏度,83%的特异性).

结论:

  • 通过传感器和心血管融合进行行走分析,有效地选肉类和CD.
  • 多式联络方法显著提高了早期检测模型的准确性.
  • 这项技术对家庭查和老龄化人口的干预非常有希望.