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

Signal and System01:26

Signal and System

1.7K
A signal x(t) is a set of data or a time function representing a variable of interest. Signals typically convey information about a phenomenon, such as atmospheric temperature, humidity, human voice, television images, a dog's bark, or birdsongs. More generally, a signal can be a function of more than one independent variable. For instance, images depend on horizontal and vertical positions and can be regarded as two-dimensional signals. However, this text will focus on one-dimensional...
1.7K

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

Machine-Learning-Assisted Screening of Electrolyte Additives for Aqueous Zinc-Ion Batteries via a Molecular Descriptor Framework.

Angewandte Chemie (International ed. in English)·2026
Same author

Design of direction-independent hydrovoltaic electricity generator based on all-foam asymmetric electrode.

Nature communications·2025
Same author

A Multifunctional Self-Charging System Based on a Compatible Electrode.

ACS applied materials & interfaces·2025
Same author

Anion Coordination Regulation with LiNO<sub>3</sub> Additive for High-Rate Low-Temperature Lithium Metal Batteries.

ACS applied materials & interfaces·2025
Same author

Photogenerated Carrier Reconstructed Ion Concentration Gradients for Moisture Electricity Generators.

Advanced materials (Deerfield Beach, Fla.)·2025
Same author

Superhydrophobic Coating with a Modified Micronano-ZnO@CoZIF-8 Structure for Efficient Anti-icing.

Langmuir : the ACS journal of surfaces and colloids·2025

相关实验视频

Updated: May 2, 2026

A Human-machine-interface Integrating Low-cost Sensors with a Neuromuscular Electrical Stimulation System for Post-stroke Balance Rehabilitation
11:06

A Human-machine-interface Integrating Low-cost Sensors with a Neuromuscular Electrical Stimulation System for Post-stroke Balance Rehabilitation

Published on: April 12, 2016

10.4K

基于深度学习辅助的应变传感阵列的多功能人机交互系统.

Hao Gu1,2, Ke Jiang2, Fei Yu1

  • 1Key Laboratory of Advanced Structural Materials, Ministry of Education & Advanced Institute of Materials Science, Changchun University of Technology, Changchun 130012, China.

ACS applied materials & interfaces
|September 26, 2024
PubMed
概括

这项研究介绍了一种使用灵活的压电传感器和深度学习的智能步行监控系统. 该系统在实时运动状态识别方面实现了高精度,使得健康状况的持续跟踪成为可能.

关键词:
卷积神经网络是一种卷积神经网络.步态分析 步态分析人与计算机的交互系统.应变感应阵列是一种应变感应阵列.远程医疗远程医疗

更多相关视频

Author Spotlight: Enhancing Grasping Abilities for Hemiplegic Patients with Flexible Robotic Limbs
03:55

Author Spotlight: Enhancing Grasping Abilities for Hemiplegic Patients with Flexible Robotic Limbs

Published on: October 27, 2023

2.1K
Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
08:15

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Published on: March 28, 2025

410

相关实验视频

Last Updated: May 2, 2026

A Human-machine-interface Integrating Low-cost Sensors with a Neuromuscular Electrical Stimulation System for Post-stroke Balance Rehabilitation
11:06

A Human-machine-interface Integrating Low-cost Sensors with a Neuromuscular Electrical Stimulation System for Post-stroke Balance Rehabilitation

Published on: April 12, 2016

10.4K
Author Spotlight: Enhancing Grasping Abilities for Hemiplegic Patients with Flexible Robotic Limbs
03:55

Author Spotlight: Enhancing Grasping Abilities for Hemiplegic Patients with Flexible Robotic Limbs

Published on: October 27, 2023

2.1K
Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
08:15

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Published on: March 28, 2025

410

科学领域:

  • 生物医学工程 生物医学工程
  • 传感器技术 传感器技术
  • 人工智能的人工智能

背景情况:

  • 持续的步态监测对于健康管理至关重要,包括手术后恢复和疾病诊断.
  • 目前的步态分析系统往往是繁的,需要专门的空间和限制现实世界的应用.

研究的目的:

  • 开发一个智能步行监测和分析系统,使用灵活的压电传感器和深度学习.
  • 为了实现实时,准确的步态数据采集和运动状态推断,用于健康监测.

主要方法:

  • 开发了灵活的压电传感器,具有高灵敏度和稳定性,并集成到鞋底.
  • 用深度学习神经网络来分析获得的步态数据.
  • 构建了一个灵活的可穿戴识别系统,具有人机交互界面.

主要成果:

  • 压电传感器表现出高灵敏度 (241.29 mV/N),快速响应,以及出色的稳定性 (R^2 = 0.9946).
  • 集成系统在实时识别人类运动状态方面实现了94.7%的准确性.
  • 该系统成功地跟踪了运动员的步态,以进行长期监测.

结论:

  • 开发的系统为日常生活中持续且可靠的步态分析提供了实用解决方案.
  • 这项技术有可能帮助个性化健康管理,早期疾病检测和远程医疗保健.