Jove
Visualize
联系我们

相关概念视频

Relative Motion Analysis using Rotating Axes - Acceleration01:22

Relative Motion Analysis using Rotating Axes - Acceleration

314
Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame. The absolute velocity of point B is determined by adding the absolute velocity of point A, the relative velocity of point B in the rotating frame, and the effects caused by the angular velocity within the rotating frame.
Time differentiation is...
314
Relative Motion Analysis - Acceleration01:10

Relative Motion Analysis - Acceleration

317
A slider-crank mechanism converts rotational motion from the crank into linear motion of the slider or vice versa. This mechanism consists of three main parts: the crank, the connecting rod, and the slider. The movement of the slider-crank is an example of general plane motion as the fluctuating angle between the crank and the connecting rod. Consider a segment AB where point A is at the end of the slider and point B is on the diametrically opposite end to point A, on a crack. The variance in...
317
Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

378
Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
Here, in order to determine the magnitude of velocity and acceleration for point...
378
Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

437
Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame.
However, to express the relative position of point B relative to point A, an additional frame of reference, denoted as x'y', is necessary. This additional frame not only translates but also rotates relative to the fixed frame, making it...
437

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

Substituent Impact on Quinoxaline Performance and Degradation in Redox Flow Batteries.

Journal of the American Chemical Society·2024
Same author

Understanding capacity fade in organic redox-flow batteries by combining spectroscopy with statistical inference techniques.

Nature communications·2023
Same author

Reducing Slip Risk: A Feasibility Study of Gait Training with Semi-Real-Time Feedback of Foot-Floor Contact Angle.

Sensors (Basel, Switzerland)·2022
Same author

Preliminary Study of Vibrotactile Feedback during Home-Based Balance and Coordination Training in Individuals with Cerebellar Ataxia.

Sensors (Basel, Switzerland)·2022
Same author

A Pilot Study Comparing the Effects of Concurrent and Terminal Visual Feedback on Standing Balance in Older Adults.

Sensors (Basel, Switzerland)·2022
Same author

Retention Effects of Long-Term Balance Training with Vibrotactile Sensory Augmentation in Healthy Older Adults.

Sensors (Basel, Switzerland)·2022
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

相关实验视频

Updated: May 17, 2025

Quantified Assessment of Infant's Gross Motor Abilities Using a Multisensor Wearable
09:24

Quantified Assessment of Infant's Gross Motor Abilities Using a Multisensor Wearable

Published on: May 17, 2024

1.2K

机器学习方法的比较分析用于用线性加速和角速率信号检测胎儿运动.

Lucy Spicher1, Carrie Bell2, Kathleen H Sienko1

  • 1Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, USA.

Sensors (Basel, Switzerland)
|May 14, 2025
PubMed
概括

使用可穿戴传感器持续监测胎儿运动,有望改善产前护理. 机器学习模型有效地从线性加速和角速率数据中检测到胎儿运动,增强胎儿健康评估.

关键词:
双向长时间短期记忆 (BiLSTM)卷积神经网络 (CNN) 是一种神经网络.胎儿监测 胎儿监测是指对胎儿进行监测.惯性测量单位 (IMU) 是指惯性测量单位.随机森林 (RF) 是一个随机的森林.频谱图是指光谱图中的光谱.时间频率分析可以穿戴的传感器.

更多相关视频

Human Fetal Blood Flow Quantification with Magnetic Resonance Imaging and Motion Compensation
06:56

Human Fetal Blood Flow Quantification with Magnetic Resonance Imaging and Motion Compensation

Published on: January 7, 2021

2.0K
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: May 17, 2025

Quantified Assessment of Infant's Gross Motor Abilities Using a Multisensor Wearable
09:24

Quantified Assessment of Infant's Gross Motor Abilities Using a Multisensor Wearable

Published on: May 17, 2024

1.2K
Human Fetal Blood Flow Quantification with Magnetic Resonance Imaging and Motion Compensation
06:56

Human Fetal Blood Flow Quantification with Magnetic Resonance Imaging and Motion Compensation

Published on: January 7, 2021

2.0K
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:

  • 生物医学工程 生物医学工程
  • 孕产妇和胎儿医学 孕产妇和胎儿医学
  • 信号处理 信号处理

背景情况:

  • 减少胎儿运动 (RFM) 是胎儿风险的关键指标,需要改善产前监测.
  • 目前的方法只提供胎儿健康的间歇性快照,需要临床设置和专家解释.
  • 需要持续,客观的监测系统来增强产前护理和早期发现胎儿痛苦.

研究的目的:

  • 探索可穿戴惯性测量单元 (IMU) 从可穿戴惯性测量单元 (IMU) 获得的线性加速和角速率数据的实用性,用于持续的胎儿运动检测.
  • 开发和比较机器学习模型,以区分胎儿运动与母亲活动.
  • 在现实世界的产前监测场景中评估不同机器学习方法的性能.

主要方法:

  • 23名参与者戴着4个腹部IMU和1个胸部参考传感器.
  • 参与者用手持按手动指示他们感知到的胎儿运动.
  • 机器学习模型 (Random Forest,BiLSTM,CNN) 使用加速度计和陀螺仪数据进行训练,包括手工设计的功能,时间序列和光谱图.

主要成果:

  • 结合加速度计和陀螺仪数据,在所有评估的机器学习模型中显著改善了胎儿运动检测.
  • 卷积神经网络 (CNN) 显示出卓越的性能,但需要更大的数据集.
  • 随机森林 (RF) 和双向长短期记忆 (BiLSTM) 模型在较小的数据集和增强的解释性下提供了强大的性能,尽管对信号噪声的敏感性增加了.

结论:

  • 可穿戴的IMU与机器学习相结合,为持续,客观的胎儿运动监测提供了一种可行的方法.
  • 机器学习模型的选择取决于数据集的大小和可解释性要求.
  • 这项技术有可能通过提供对胎儿福祉的持续洞察来彻底改变产前护理.