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

Enhancing Real-World Fall Detection Using Commodity Devices: A Systematic Study.

Sensors (Basel, Switzerland)·2025
Same author

AI-Generated Fall Data: Assessing LLMs and Diffusion Model for Wearable Fall Detection.

Sensors (Basel, Switzerland)·2025
Same author

Experimental Study of Long Short-Term Memory and Transformer Models for Fall Detection on Smartwatches.

Sensors (Basel, Switzerland)·2024
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: Sep 11, 2025

Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.8K

通过合成数据增强可穿戴式摔倒检测系统.

Minakshi Debnath1, Sana Alamgeer1, Md Shahriar Kabir1

  • 1Department of Computer Science, Texas State University, San Marcos, TX 78666-4684, USA.

Sensors (Basel, Switzerland)
|August 14, 2025
PubMed
概括
此摘要是机器生成的。

使用扩散模型生成现实的合成落数据显著提高了深度学习模型用于落检测的性能. 这种方法提高了线下准确度和实时检测率,解决了临床环境中的数据稀缺问题.

关键词:
扩散扩散是一种扩散.落检测系统 落检测系统 落检测系统合成数据的生成.时间序列数据数据时间序列数据视频提取 视频提取

更多相关视频

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

4.1K
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

9.0K

相关实验视频

Last Updated: Sep 11, 2025

Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.8K
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

4.1K
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

9.0K

科学领域:

  • 生物医学工程 生物医学工程
  • 医疗保健中的人工智能
  • 数据科学数据科学数据科学

背景情况:

  • 深度学习模型需要大量的训练数据,但在临床应用中,这些数据往往是有限的,特别是用于落检测.
  • 获得多样化和全面的真实世界落数据集是一个重大挑战,阻碍了强大的检测系统的发展.

研究的目的:

  • 研究生成现实的合成多变量落数据的方法,以增强有限的现实世界数据集.
  • 评估基于扩散的生成AI和基于视频的姿势估计对合成落数据生成的有效性.
  • 评估合成数据对深度学习模型用于摔倒检测的性能的影响.

主要方法:

  • 应用了传统的时间序列增强,基于扩散的生成AI和基于视频的姿势估计,以从公共镜头中提取下降段.
  • 使用扩散模型和基于视频的姿势估计生成合成多变量落数据,针对特定的传感器放置量身定制.
  • 通过使用定量指标 (FID,区分得分,预测得分,JSD,KS测试) 和视觉检查评估合成数据质量.

主要成果:

  • 基于扩散的合成产生了最现实的和分布一致的合成落数据.
  • 纳入基于扩散的合成数据将长期短期记忆 (LSTM) 模型的离线F1分数提高了7-10%.
  • 使用SmartFall应用程序测试的实时落检测性能,通过包含合成数据,提高了24%.

结论:

  • 合成数据生成,特别是使用扩散模型,是克服临床落检测数据局限性的可行策略.
  • 开发的方法创造了现实的和多样化的合成落数据,提高了落检测模型的稳定性和现实世界的适用性.
  • 这项研究强调了利用生成性人工智能和视频分析来改进摔倒检测系统的有希望但尚未探索的方向.