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

Distance Measurements by Taping01:18

Distance Measurements by Taping

97
Tapes are essential in surveying for accurate, durable, and short-distance measurements. Made from lightweight, nylon-coated steel, they offer flexibility and strength for rugged outdoor use. The nylon coating protects against rust and wear, extending the tape's life. Standard lengths, around 30 meters, are marked in meters and millimeters for precision.Surveyors select tapes based on site conditions and accuracy needs. Lightweight, nylon-coated tapes are commonly used for ease of handling and...
97
Force Classification01:22

Force Classification

1.6K
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,...
1.6K
Classification of Signals01:30

Classification of Signals

878
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
878
Errors in Taping01:18

Errors in Taping

78
Errors in taping arise from multiple factors that can significantly impact measurement accuracy in surveying. Misalignment of the tape, often due to human error, is one primary source. A skilled rear tapeman, using a telescope, can help correct alignment by guiding the head tapeman; however, human limitations still lead to small inaccuracies. These errors may include misplacement of pins or inaccurate tape readings due to common visual confusions, such as mistaking a six for a nine. Such...
78
Detection of Black Holes01:10

Detection of Black Holes

2.3K
Although black holes were theoretically postulated in the 1920s, they remained outside the domain of observational astronomy until the 1970s.
Their closest cousins are neutron stars, which are composed almost entirely of neutrons packed against each other, making them extremely dense. A neutron star has the same mass as the Sun but its diameter is only a few kilometers. Therefore, the escape velocity from their surface is close to the speed of light.
Not until the 1960s, when the first neutron...
2.3K

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

Movement-Based Low Back Pain Subgroups Using Motion Tape Strain Data with Biomechanical and Causal Feature Engineering.

Sensors (Basel, Switzerland)·2026
Same author

Corrigendum: Carbon nanotube thin film strain sensors: comparison between experimental tests and numerical simulations (2017<i>Nanotechnology</i><b>28</b>155502).

Nanotechnology·2026
Same author

Assessing Low Back Movement with Motion Tape Sensor Data Through Deep Learning.

Sensors (Basel, Switzerland)·2026
Same author

Carbon Nanotube Elastic Fabric Motion Tape Sensors for Low Back Movement Characterization.

Sensors (Basel, Switzerland)·2025
Same author

Progressive and Asymmetrical Deadlift Loads Captured by Wearable Motion Tape Sensors.

Sensors (Basel, Switzerland)·2024
Same author

Motion Tape Strain During Trunk Muscle Engagement in Young, Healthy Participants.

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 9, 2025

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.5K

使用运动磁带和机器学习进行拳击击探测和分类

Shih-Chao Huang1, Taylor Pierce2, Yun-An Lin1

  • 1Active, Responsive, Multifunctional, and Ordered-Materials Research (ARMOR) Laboratory, Department of Structural Engineering, University of California San Diego, La Jolla, CA 92093, USA.

Sensors (Basel, Switzerland)
|August 28, 2025
PubMed
概括
此摘要是机器生成的。

机器学习使用可穿戴运动带传感器的皮肤应变数据准确分类拳击拳击. 这项技术有助于分析体育和生物力学的运动表现.

关键词:
开始时间迷你火箭分类方式移动运动监督学习时间序列变压器培训工作可穿戴式传感器

更多相关视频

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

9.1K
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

相关实验视频

Last Updated: Sep 9, 2025

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.5K
Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

9.1K
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

科学领域:

  • 运动科学
  • 生物力学
  • 可穿戴技术

背景情况:

  • 拳击表现分析通常依赖于主观观察或复杂的设备.
  • 穿戴式传感器为客观,实时的数据收集提供了潜在的解决方案.

研究的目的:

  • 使用机器学习算法对拳击类型进行分类.
  • 评估穿戴式传感器 (运动带) 在拳击运动中捕获皮肤应变数据的有效性.

主要方法:

  • 进行了一项涉及拳击训练的人体研究.
  • 试验对象使用重袋或不使用重袋进行冲刺和.
  • 使用运动带收集皮肤应变时间历史数据,并通过时间序列分类算法处理.

主要成果:

  • 机器学习模型成功地根据皮肤应变测量分类了不同的冲击类型.
  • 运动带系统在区分各种冲击和条件方面表现出有效性.

结论:

  • 穿戴式运动带传感器与机器学习相结合, 提供了有效的拳击击击分类方法.
  • 这个系统显示出在体育和生物力学中客观地分析人类表现的巨大潜力.