Search research articles

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 Journal Methods Collections JoVE Encyclopedia of Experiments 存档

教育

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual 教师资源中心教师网站

使用条款与条件

相关概念视频

Steps in Outbreak Investigation

Steps in Outbreak Investigation

In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:

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

Behavioral characterization of olfactory, auditory, and motor deficits in 5×FAD mice.

Journal of veterinary science·2026

Same author

Spicy food intake and dietary factors shape the gut microbiome and metabolism of mucin and short-chain fatty acids in healthy adults.

Scientific reports·2026

Same author

Evaluation of cavitation enhancements in low-boiling point (< -2°C) perfluorocarbon nanodroplet and microbubble mixtures using therapeutic ultrasound pulses.

Ultrasonics sonochemistry·2026

Same author

Streamlined Facial Data Collection Based on Utterance and Emotional Data for Human-to-Avatar Reconstruction.

IEEE transactions on visualization and computer graphics·2026

Same author

Discovery of key surface electromyography features during walking for discerning high and low muscle mass using machine learning analysis.

Scientific reports·2026

Same author

Diverse cultivation strategies are necessary to capture microbial diversity in High Arctic lake sediment.

Frontiers in microbiomes·2026

Search research articles

相关实验视频

Updated: May 2, 2026

Automated Gait Analysis in Mice with Chronic Constriction Injury

Automated Gait Analysis in Mice with Chronic Constriction Injury

Published on: October 17, 2017

使用传感器遗传内的机器学习算法对异常步态进行分类.

Beomjoon Park, Minhye Kim, Dawoon Jung

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|March 5, 2025

概括

本研究介绍了使用内传感器的高效步态分析方法,为传统系统提供了切实可行的替代方案. 机器学习模型,特别是极端梯度提升,在分类步态模式方面取得了很高的准确性.

更多相关视频

Clinical-oriented Three-dimensional Gait Analysis Method for Evaluating Gait Disorder

Clinical-oriented Three-dimensional Gait Analysis Method for Evaluating Gait Disorder

Published on: March 4, 2018

Gait Analysis of Age-dependent Motor Impairments in Mice with Neurodegeneration

Gait Analysis of Age-dependent Motor Impairments in Mice with Neurodegeneration

Published on: June 18, 2018

相关实验视频

Last Updated: May 2, 2026

Automated Gait Analysis in Mice with Chronic Constriction Injury

Automated Gait Analysis in Mice with Chronic Constriction Injury

Published on: October 17, 2017

Clinical-oriented Three-dimensional Gait Analysis Method for Evaluating Gait Disorder

Clinical-oriented Three-dimensional Gait Analysis Method for Evaluating Gait Disorder

Published on: March 4, 2018

Gait Analysis of Age-dependent Motor Impairments in Mice with Neurodegeneration

Gait Analysis of Age-dependent Motor Impairments in Mice with Neurodegeneration

Published on: June 18, 2018

科学领域:

数字健康数字健康
生物医学工程生物医学工程
运动科学运动科学运动科学

背景情况:

传统的步态分析方法 (如3D运动捕捉) 昂贵且耗时.
在数字健康领域,对实用,日常步态监测解决方案的需求日益增长.

研究的目的:

开发和评估一种高效的方法,以使用传感器支持的内来评估步行表现.
为了比较各种机器学习模型的表现,用于步态模式分类.

主要方法:

收集了54名受试者的步态数据,在各种步态模式中使用6个内压力传感器.
处理传感器数据以提取36个重要的步态参数.
开发并测试了分类模型,包括支持向量机,随机森林,极端梯度增强和k-最近邻居.

主要成果:

极端梯度提升显示了优越的分类性能.
极端梯度提升模型在样本级别达到0.76的精度,在受试者级别达到0.85.
确定了有效的步态分析的36个重要参数.

结论:

提出的基于内的步态分析方法是传统方法的高效和可行的替代方案.
这项技术有可能通过改进步态监测来增强骨科和康复领域的患者护理.
该研究支持数字健康工具的整合,以提供可访问和准确的生物力学评估.