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

Feature-Based Normality Models for Anomaly Detection.

Sensors (Basel, Switzerland)·2025
Same author

Impact of Surveillance Colonoscopy Timing on Colorectal Cancer Incidence After Advanced Adenoma Resection.

The American journal of gastroenterology·2025
Same author

Decentralised Global Service Discovery for the Internet of Things.

Sensors (Basel, Switzerland)·2024
Same author

Human Behavior Recognition via Hierarchical Patches Descriptor and Approximate Locality-Constrained Linear Coding.

Sensors (Basel, Switzerland)·2023
Same author

Comparison of endoscopic submucosal resection with ligation and endoscopic submucosal dissection for small rectal neuroendocrine tumors: A multicenter retrospective study.

DEN open·2022
Same author

Adaptive Wireless Network Management with Multi-Agent Reinforcement Learning.

Sensors (Basel, Switzerland)·2022
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 3, 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

2.6K

基于传感器识别人类活动的机器学习技术与数据异质性-一篇评论

Xiaozhou Ye1, Kouichi Sakurai2, Nirmal-Kumar C Nair1

  • 1Department of Electrical, Computer, and Software Engineering, The University of Auckland, Auckland 1010, New Zealand.

Sensors (Basel, Switzerland)
|January 8, 2025
PubMed
概括
此摘要是机器生成的。

本综述探讨了机器学习如何解决人类活动识别 (HAR) 中的数据异质性. 解决各种传感器数据分布可以提高HAR模型的性能和个性化.

关键词:
数据异质性数据异质性数据没有被分发出来.人类活动的认可 人类活动的认可时间序列分类时间序列分类.

更多相关视频

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment
06:49

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Published on: December 11, 2015

8.9K
Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.6K

相关实验视频

Last Updated: Jun 3, 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

2.6K
Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment
06:49

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Published on: December 11, 2015

8.9K
Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.6K

科学领域:

  • 无处不在的计算无处不在的计算
  • 机器学习 机器学习
  • 传感器数据分析数据分析

背景情况:

  • 人类活动识别 (HAR) 对于使用多维传感器数据分析行为至关重要.
  • 当前的HAR研究往往忽略了跨数据集的数据分布变化,限制了现实世界的适用性.
  • 传感器数据中的数据异质性对HAR模型的概括和效率构成重大挑战.

研究的目的:

  • 审查机器学习方法,以解决人类活动识别 (HAR) 中的数据异质性.
  • 在HAR中遇到的数据异质性类型的分类.
  • 确定适合的机器学习方法和数据集,用于异构的HAR.

主要方法:

  • 在基于传感器的HAR中对数据异质性类型的分类.
  • 识别和应用针对特定异质性挑战的机器学习方法.
  • 对现有的HAR数据集及其特征进行系统审查.
  • 讨论未来的研究方向和面临的挑战.

主要成果:

  • 确定了影响HAR性能的不同类型的数据异质性.
  • 突出了有效缓解这些异质性问题的机器学习技术.
  • 提供了有关HAR数据集的摘要,并指出了它们的分布性质.
  • 强调了解决异质性的好处,包括提高准确性和减少计算负载.

结论:

  • 机器学习提供了可行的解决方案,以克服HAR中的数据异质性.
  • 解决数据分布变异是开发强大,个性化和高效的HAR系统的关键.
  • 需要进一步的研究来探索HAR的先进适应式和联合式学习方法.