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

Coordinated human-exoskeleton locomotion emerges from regulating virtual energy.

PloS one·2025
Same author

Visual feedback decoding during bimanual circle drawing.

Journal of neurophysiology·2023
Same author

Quantitative Modeling of Spasticity for Clinical Assessment, Treatment and Rehabilitation.

Sensors (Basel, Switzerland)·2020
Same author

Knee Implant Loosening Detection: A Vibration Analysis Investigation.

Annals of biomedical engineering·2017
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
查看所有相关文章

相关实验视频

Updated: Jun 27, 2025

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
11:25

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

43.3K

基于IMU的步行阶段实时估计使用多分辨率神经网络.

Lyndon Tang1, Mohammad Shushtari1, Arash Arami1,2

  • 1Department of Mechanical and Mechatronics Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada.

Sensors (Basel, Switzerland)
|April 27, 2024
PubMed
概括
此摘要是机器生成的。

本研究引入了使用惯性测量单位 (IMU) 的实时步行阶段估计器. 该模型准确地估计了不同步行条件和步行速度的步行阶段,显示了临床应用的前景.

关键词:
步态阶段估计步态阶段估计步态的变化 步态的变化惯性测量单位是一种惯性测量单位.

更多相关视频

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
10:28

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published on: July 24, 2019

15.2K
Engineering Platform and Experimental Protocol for Design and Evaluation of a Neurally-controlled Powered Transfemoral Prosthesis
11:16

Engineering Platform and Experimental Protocol for Design and Evaluation of a Neurally-controlled Powered Transfemoral Prosthesis

Published on: July 22, 2014

16.3K

相关实验视频

Last Updated: Jun 27, 2025

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
11:25

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

43.3K
Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
10:28

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published on: July 24, 2019

15.2K
Engineering Platform and Experimental Protocol for Design and Evaluation of a Neurally-controlled Powered Transfemoral Prosthesis
11:16

Engineering Platform and Experimental Protocol for Design and Evaluation of a Neurally-controlled Powered Transfemoral Prosthesis

Published on: July 22, 2014

16.3K

科学领域:

  • 生物力学 生物力学
  • 机器人技术 机器人技术 机器人技术
  • 机器学习 机器学习

背景情况:

  • 准确的步行阶段估计对于分析人类运动和开发辅助技术至关重要.
  • 当前的方法可能在各种走路条件和个体步行模式中缺乏稳健性.

研究的目的:

  • 开发和验证使用可穿戴惯性测量单元 (IMU) 的实时步行阶段估计器.
  • 评估模型在不同参与者,步行速度和异常步行模式的概括性.

主要方法:

  • 一个多速卷积神经网络 (CNN) 被训练使用大腿和部安装IMU的数据.
  • 该模型使用单个实验对象的交叉验证进行了评估,并对各种步行速度和条件进行了测试,包括不对称步行和停止启动场景.
  • 使用统计测试 (例如,Kolmogorov-Smirnov) 来确认性能稳定性.

主要成果:

  • 步态阶段估计器在脚跟撞击时实现了空间根平均平方误差5.00±1.65%和时间平均绝对误差2.78±0.97%.
  • 交叉验证表明,当排除特定的行走条件或对新参与者的测试时,没有显著的性能下降.
  • 对于不包括在训练组中的异常行走条件,没有观察到显著的错误增加.

结论:

  • 拟议的基于IMU的步行阶段估计器在各种参与者和行走场景中表现出强大的概括性.
  • 这项技术具有临床步态分析的潜力,特别是对于病态步态患者群体.
  • 这些发现支持使用这个估计器来推进机器人辅助行走和康复.