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

Classification of Bones01:18

Classification of Bones

7.3K
The bones of the human skeletal system are of varied shapes, sizes, and functions. They can be classified based on their shape and function into four major classes: long bones, short bones, flat bones, and irregular bones. Some classifications include a fifth type, the sesamoid bones, as a separate class, whereas others categorize them under short bones.
Long and Short Bones
The appendicular skeleton, particularly the upper and lower limbs, is primarily made of long and short bones. The...
7.3K

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

Energy-Efficient Resource Allocation Scheme Based on Reinforcement Learning in Distributed LoRa Networks.

Sensors (Basel, Switzerland)·2025
Same author

A Non-Invasive Approach for Facial Action Unit Extraction and Its Application in Pain Detection.

Bioengineering (Basel, Switzerland)·2025
Same author

Accurate Cardiac Duration Detection for Remote Blood Pressure Estimation Using mm-Wave Doppler Radar.

Sensors (Basel, Switzerland)·2025
Same author

A Comparison Study of Person Identification Using IR Array Sensors and LiDAR.

Sensors (Basel, Switzerland)·2025
Same author

Non-Contact Cross-Person Activity Recognition by Deep Metric Ensemble Learning.

Bioengineering (Basel, Switzerland)·2024
Same author

Heart Rate Estimation Considering Reconstructed Signal Features Based on Variational Mode Decomposition via Multiple-Input Multiple-Output Frequency Modulated Continuous Wave Radar.

Sensors (Basel, Switzerland)·2024

相关实验视频

Updated: Sep 16, 2025

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
09:41

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Published on: April 21, 2023

1.7K

使用FMCW雷达进行人类骨估计的新型深度学习模型.

Parma Hadi Rantelinggi1,2, Xintong Shi1, Mondher Bouazizi3

  • 1Graduate School of Science and Technology, Keio University, Yokohama 223-8522, Japan.

Sensors (Basel, Switzerland)
|July 12, 2025
PubMed
概括

这项研究引入了一个新的深度学习框架,用于使用频率调制连续波 (FMCW) 雷达进行精确的人类骨估计,以稀少的数据改进隐私保护运动分析.

关键词:
在FMCW雷达.人类运动分析分析多头注意力多头注意力一个点云,一个点云.骨架检测检测器可以检测骨.

更多相关视频

Author Spotlight: Three-Dimensional Cephalometric Landmark Annotation Demonstration on Human Cone Beam Computed Tomography Scans
10:23

Author Spotlight: Three-Dimensional Cephalometric Landmark Annotation Demonstration on Human Cone Beam Computed Tomography Scans

Published on: September 8, 2023

3.1K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.9K

相关实验视频

Last Updated: Sep 16, 2025

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
09:41

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Published on: April 21, 2023

1.7K
Author Spotlight: Three-Dimensional Cephalometric Landmark Annotation Demonstration on Human Cone Beam Computed Tomography Scans
10:23

Author Spotlight: Three-Dimensional Cephalometric Landmark Annotation Demonstration on Human Cone Beam Computed Tomography Scans

Published on: September 8, 2023

3.1K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.9K

科学领域:

  • 机器人和自动化 机器人和自动化
  • 生物医学工程 生物医学工程
  • 计算机视觉 计算机视觉

背景情况:

  • 频率调制连续波 (FMCW) 雷达提供保护隐私的人类运动分析.
  • 现有的骨架估计方法面临着稀疏的雷达点云数据的挑战,影响关节定位的准确性.

研究的目的:

  • 开发一种新的深度学习框架,使用FMCW雷达数据进行增强的人类骨估计.
  • 为了提高关节定位在保护隐私的动作分析中的准确性和稳定性.

主要方法:

  • 一个深度学习框架,集成卷积神经网络 (CNN),多头变压器和Bi-LSTM网络.
  • 一个框架连接策略,在神经网络处理之前提高数据质量.
  • 使用MARS数据集进行实验评估.

主要成果:

  • 与传统方法相比,拟议的模型显著减少了估计误差.
  • 获得了1.77厘米的平均绝对误差 (MAE) 和2.92厘米的根平均平方误差 (RMSE).
  • 证明了计算效率与提高准确性的同时.

结论:

  • 新的深度学习框架有效地解决了稀疏的雷达数据对人类骨估计的局限性.
  • 这种方法增强了时空特征表示,从而导致更准确,更可靠的运动分析.
  • 该方法显示了保护隐私的人类运动跟踪应用程序的巨大潜力.