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

Muscle Recovery and Fatigue01:24

Muscle Recovery and Fatigue

4.2K
Muscle fatigue refers to the decline in a muscle's ability to maintain the force of contraction after prolonged activity. It primarily stems from changes within muscle fibers. Even before experiencing muscle fatigue, one may feel tired and have the urge to stop the activity. This response, known as central fatigue, occurs due to changes in the central nervous system, namely the brain and spinal cord. While there is no single mechanism that induces fatigue, it may serve as a protective...
4.2K
Fatigue01:21

Fatigue

831
Fatigue occurs when materials rupture under repeated or fluctuating loads, even at stress levels far below their static breaking strength. It typically results in brittle failure, even for ductile materials. It is a critical consideration in designing machines and structural components subjected to repetitive or varying loads. The nature of these loadings can range from fluctuating loads like unbalanced pump impellers causing vibrations to repeatedly bending a thin steel rod wire back and forth...
831
Healthcare Agencies II01:17

Healthcare Agencies II

1.1K
There are various healthcare agencies in the United States—some of which are managed by religious institutions and others by different government branches.
Parish nursing is a growing specialty nursing profession that focuses on holistic healthcare, health promotion, and illness prevention. It blends professional nursing practice with a health ministry, focusing on health and healing within the context of a Christian community. Parish nurses serve as health educators, referral sources,...
1.1K
Secondary Healthcare System01:11

Secondary Healthcare System

2.0K
Secondary healthcare is offered by a specialist, generally in hospitals or clinics for patients referred by primary healthcare providers. It occurs when a person has an illness or injury that requires specific medical care. Secondary care is often referred to as acute care. Secondary care can range from uncomplicated care to repair a minor laceration or treat a strep throat infection to more complicated emergent care, such as treating a head injury sustained in an automobile accident. Whatever...
2.0K
Tertiary Healthcare System01:21

Tertiary Healthcare System

2.2K
Specialized care provided over an extended period is called tertiary care. Usually, a primary or secondary care physician will refer a patient to tertiary care. A patient's maximum physical and mental function is restored in tertiary care, which is caused due to the impact of a chronic illness or condition. Tertiary care aims to achieve the highest level of functioning possible while managing chronic illness. For example, a patient who falls and fractures their hip will need secondary care...
2.2K
Healthcare Agencies I01:18

Healthcare Agencies I

1.2K
Healthcare agencies provide healthcare services to people. In the United States, voluntary agencies are often non-profit centers sponsored by donations, grants, or fundraisers. One such organization is Meals on Wheels, which provides meals to the elderly and homebound. The American Heart Association and the American Lung Association are other non-profit community organizations. Doctors and nurses are frequently active members of these organizations, which offer health checks and educational...
1.2K

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

Radar-Based Activity Recognition in Strictly Privacy-Sensitive Settings Through Deep Feature Learning.

Biomimetics (Basel, Switzerland)·2025
Same author

A Systematic Review of Surface Electromyography in Sarcopenia: Muscles Involved, Signal Processing Techniques, Significant Features, and Artificial Intelligence Approaches.

Sensors (Basel, Switzerland)·2025
Same author

A Transfer Learning Approach for Toe Walking Recognition Using Surface Electromyography on Leg Muscles.

Sensors (Basel, Switzerland)·2025
Same author

Preliminary Study on Wearable Smart Socks with Hydrogel Electrodes for Surface Electromyography-Based Muscle Activity Assessment.

Sensors (Basel, Switzerland)·2025
Same author

Exploring Dance as a Therapeutic Approach for Parkinson Disease Through the Social Robotics for Active and Healthy Ageing (SI-Robotics): Results From a Technical Feasibility Study.

JMIR aging·2025
Same author

Movement Disorders and Smart Wrist Devices: A Comprehensive Study.

Sensors (Basel, Switzerland)·2025
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: Jan 29, 2026

Muscle Function Obtained with Motion Mode Ultrasound and Surface Electromyography during Core Endurance Exercise
09:21

Muscle Function Obtained with Motion Mode Ultrasound and Surface Electromyography during Core Endurance Exercise

Published on: August 25, 2022

3.8K

在医疗保健应用中通过使用表面肌电图进行肌肉疲劳评估:一种转移学习方法.

Andrea Manni1, Gabriele Rescio1, Andrea Caroppo1

  • 1Institute for Microelectronics and Microsystems, National Research Council of Italy, 73100 Lecce, Italy.

Sensors (Basel, Switzerland)
|January 28, 2026
PubMed
概括
此摘要是机器生成的。

这项研究开发了一种深度学习框架,用于使用电肌图 (EMG) 信号监测老年人肌肉疲劳. 这种新的方法准确地分类了疲劳水平,提高了辅助生活应用的安全性.

关键词:
肌肉疲劳 肌肉疲劳 肌肉疲劳表面电力学图 (surface electromyography) 是一种表面电力学图.转移学习转移学习

更多相关视频

Acquisition and Semi-Automated Analysis of Respiratory Muscle Surface Electromyography
09:42

Acquisition and Semi-Automated Analysis of Respiratory Muscle Surface Electromyography

Published on: January 24, 2025

1.3K
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

1.2K

相关实验视频

Last Updated: Jan 29, 2026

Muscle Function Obtained with Motion Mode Ultrasound and Surface Electromyography during Core Endurance Exercise
09:21

Muscle Function Obtained with Motion Mode Ultrasound and Surface Electromyography during Core Endurance Exercise

Published on: August 25, 2022

3.8K
Acquisition and Semi-Automated Analysis of Respiratory Muscle Surface Electromyography
09:42

Acquisition and Semi-Automated Analysis of Respiratory Muscle Surface Electromyography

Published on: January 24, 2025

1.3K
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

1.2K

科学领域:

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

背景情况:

  • 肌肉疲劳监测对于老年人的安全和活动支持至关重要.
  • 现有的疲劳评估方法在实时应用方面存在局限性.
  • 环境辅助生活 (AAL) 需要非侵入性,可靠的监测解决方案.

研究的目的:

  • 引入一种新的深度学习框架来对肌肉疲劳水平进行分类.
  • 为了利用无线表面电肌图 (sEMG) 数据来检测疲劳.
  • 支持开发用于老年护理的AAL应用程序.

主要方法:

  • 从老年人和非老年人中收集了一个新的sEMG信号数据集.
  • 一维sEMG信号被转换成二维时间频率图像 (scalograms) 使用连续波形变换.
  • 预先训练的卷积神经网络 (CNN) 被微调为图像分类,包括二进制和多类疲劳水平检测.

主要成果:

  • 深度学习框架在二进制分类中实现了98.6%的准确性 (疲劳与非疲劳).
  • 多类分类实现了95.6%的准确性 (没有疲劳,中度疲劳,强度疲劳).
  • 拟议的转移学习管道超过了传统的机器学习方法 (最大92%的准确性).

结论:

  • 拟议的深度学习框架显示了对肌肉疲劳监测的强大和可泛化的性能.
  • 这种方法为AAL场景提供了一个潜在的实时,非侵入性解决方案.
  • 这些发现支持将先进的人工智能整合到高级老年护理和安全中.