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

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

122
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
122
Force Classification01:22

Force Classification

1.2K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
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

Multimodal healthcare system for human activity recognition using multiple features and advanced ensemble classifier.

Digital health·2026
Same author

Toward intelligent rehabilitation: Multimodal human pose modeling with parametric meshes and graph-based temporal reasoning.

Digital health·2026
Same author

Deep locomotion prediction learning over biosensors, ambient sensors, and computer vision.

PloS one·2026
Same author

Correction: Multi-modal remote sensory learning for multi-objects over autonomous devices.

Frontiers in bioengineering and biotechnology·2025
Same author

Deep multimodal biomechanical analysis for lower back pain rehabilitation to improve patients stability.

Frontiers in bioengineering and biotechnology·2025
Same author

Multimodal image fusion for enhanced vehicle identification in intelligent transport.

PeerJ. Computer science·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: Jul 15, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

3.8K

通过基于物联网的多模式深度学习框架智能ADL识别.

Madiha Javeed1, Naif Al Mudawi2, Abdulwahab Alazeb2

  • 1Department of Computer Science, Air University, E-9, Islamabad 44000, Pakistan.

Sensors (Basel, Switzerland)
|September 28, 2023
PubMed
概括
此摘要是机器生成的。

这项研究引入了使用物联网 (IoT) 设备的智能家居监控系统,以远程跟踪老年人的日常生活活动 (ADL). 多式联络方法在识别ADL时实现了84.14%的准确性.

关键词:
这就是为什么物联网物联网物联网.活动的日常生活的认可活动.深度学习是一种深度学习.多式联运数据是多式联运数据.对患者进行监测和监测.智能家居是一个智能家居.

更多相关视频

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.2K
Author Spotlight: Advancing Alzheimer's Research &#8211; Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.1K

相关实验视频

Last Updated: Jul 15, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

3.8K
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.2K
Author Spotlight: Advancing Alzheimer's Research &#8211; Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.1K

科学领域:

  • 计算机科学 计算机科学
  • 老年学是一门学科.
  • 生物医学工程 生物医学工程

背景情况:

  • 远程监控系统对于老年护理至关重要,为家庭和护理人员提供灵活性.
  • 日常生活活动 (ADLs) 为监测老年人和患者提供了有效的指标.
  • 现有的系统通常依赖于单一类型的传感器,限制了全面的监控.

研究的目的:

  • 通过多感应物联网 (IoT) 设备为远程老年人监控提供强大的分层架构.
  • 开发一种多式联络方法,整合可穿戴传感器和视频数据,以加强ADL识别.
  • 实现对老年人的日常活动进行准确可靠的远程监控.

主要方法:

  • 一个分层的架构处理来自多式联网物联网传感器 (可穿戴惯性传感器,视频) 的数据.
  • 预处理步骤包括数据过,细分,地标检测和2D棒模型创建.
  • 进行特征提取,融合和优化,然后使用卷积神经网络 (CNN) 进行分类.

主要成果:

  • 拟议的多式联运系统有效地融合了来自不同传感器的数据.
  • 层次架构成功处理和分析了用于ADL识别的传感器数据.
  • 在识别日常生活活动方面,获得了84.14%的可接受平均准确率.

结论:

  • 开发的基于物联网的多模式分层系统证明了远程监控老年人的可行解决方案.
  • 多感官数据和深度学习的整合提高了ADL识别的准确性.
  • 这种方法为改善老年人在家中的安全和福祉提供了一个有希望的方向.