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

The epidemiological burden and societal cost of 14 respiratory conditions in the World Health Organization European region: systematic evidence map and economic analysis.

ERJ open research·2026
Same author

Use of Wearable Technology for Measuring and Characterizing Sedentary Behavior in People With Mild Cognitive Impairment and Dementia: Systematic Review.

JMIR aging·2026
Same author

Classifying mental stress from eye tracking data: deep learning approaches for out-of-the-lab conditions.

Scientific reports·2026
Same author

Temperature exposure and time adolescents spend in physical activity across intensity levels.

Environmental epidemiology (Philadelphia, Pa.)·2026
Same author

Development of an artificial intelligence prediction model for moderate-to-severe COPD exacerbations using continuous multiple unobtrusive sensors: protocol of a multicentre prospective observational study.

BMJ open respiratory research·2026
Same author

Laboratory-based turning performance during walking in people with mild cognitive impairment and dementia.

Journal of Alzheimer's disease : JAD·2026
Same journal

Stakeholder Experiences With the Pneumococcal Conjugate Vaccine Chatbot as a Complementary Capacity-Building Tool for Frontline Health Workers in India: Qualitative Study.

JMIR formative research·2026
Same journal

Acceptability and Perceived Usefulness of a Digital Gambling Harm Minimisation Tool: A Cross-Sectional Study.

JMIR formative research·2026
Same journal

Knowledge Graphs Based on Meta-Analysis Papers Improve the Quality of Case Formulation: Mixed Methods Design.

JMIR formative research·2026
Same journal

Expedited Transition to Digital Delivery of Recovery Support Services Due to the COVID-19 Pandemic: Mixed Methods Needs Assessment.

JMIR formative research·2026
Same journal

Impact of an mHealth App on Digital Transformation: Randomized Clinical Trial on Strengthening Digital Skills in Older Women.

JMIR formative research·2026
Same journal

Emotion Classification in Japanese Cancer Survivor Interview Narratives Using Sentiment Polarity and Plutchik Emotion Frameworks: Model Development and Evaluation Study.

JMIR formative research·2026
查看所有相关文章

相关实验视频

Updated: Jun 27, 2025

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

使用手腕穿戴的惯性传感器进行现实世界步态检测:验证研究

Felix Kluge1, Yonatan E Brand2, M Encarna Micó-Amigo3

  • 1Novartis Biomedical Research, Novartis Pharma AG, Basel, Switzerland.

JMIR formative research
|May 1, 2024
PubMed
概括
此摘要是机器生成的。

佩戴在手腕上的传感器可以在现实环境中检测步态序列,帮助进行移动性分析. 然而,腰部传感器在不同患者群体的步态检测中提供了更高的准确性.

关键词:
动员-D 动员-D 动员加速度计的加速计是什么?数字健康数字健康数字移动性的结果.惯性测量单位是一种惯性测量单位.验证验证的时间走路走路,走路走路,走路走路.可穿戴式传感器传感器

更多相关视频

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment
06:49

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Published on: December 11, 2015

8.9K
Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior
10:52

Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior

Published on: April 13, 2016

8.8K

相关实验视频

Last Updated: Jun 27, 2025

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
Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment
06:49

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Published on: December 11, 2015

8.9K
Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior
10:52

Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior

Published on: April 13, 2016

8.8K

科学领域:

  • 数字健康数字健康
  • 生物医学工程 生物医学工程
  • 康复技术 康复技术 康复技术

背景情况:

  • 戴在手腕上的惯性传感器对于数字健康中的真实世界移动性评估至关重要.
  • 步态检测算法对于分析长期传感器数据至关重要,但手臂运动使基于手腕的检测变得复杂.
  • 缺乏跨不同患者群体和传感器位置的手腕穿戴步态检测算法的比较验证.

研究的目的:

  • 通过使用现实世界的数据,验证手腕佩戴传感器的步态序列 (GS) 检测算法.
  • 为了比较手腕佩戴传感器算法的性能与背部佩戴传感器的性能.

主要方法:

  • 83名参与者 (包括患有帕金森病,多发性硬化症,关节骨折恢复,慢性肺炎,心力衰竭和健康的老年人) 佩戴了手腕,腰部和脚的惯性传感器.
  • 一个多传感器参考系统 (包括压力内和红外距离传感器) 用于验证.
  • 十个基于手腕的步态检测算法得到了验证,并与基于下背的算法进行了比较.

主要成果:

  • 最好的基于手腕的算法实现了0.55-0.81之间的平均灵敏度和0.95-0.98.8之间的特异性.
  • 最好的手腕算法估计的步行时间误差在8.9%至32.7%之间.
  • 腰部传感器表现出卓越的性能,平均灵敏度为0.71-0.91,特异性为0.96-0.99,步行时间误差为6.3%-23.5%,特别是在严重步行障碍患者中.

结论:

  • 佩戴在手腕上的传感器可以在现实场景中有效地检测步态序列,从而促进步态参数提取.
  • 该研究提供了关于临床步态研究中传感器放置的知情决策的证据.
  • 低背部传感器的放置通常会比手腕的放置更高的步态检测准确度,特别是在复杂的患者群体中.