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

Force Classification01:22

Force Classification

1.2K
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,...
1.2K
Motor Units00:46

Motor Units

58.3K
A motor unit consists of two main components: a single efferent motor neuron (i.e., a neuron that carries impulses away from the central nervous system) and all of the muscle fibers it innervates. The motor neuron may innervate multiple muscle fibers, which are single cells, but only one motor neuron innervates a single muscle fiber.
58.3K
Classification of Signals01:30

Classification of Signals

461
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...
461
Aggregates Classification01:29

Aggregates Classification

325
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
325
Classification of Systems-I01:26

Classification of Systems-I

186
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
186
Classification of Systems-II01:31

Classification of Systems-II

146
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
146

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

MYFix: Automated Fixation Annotation of Eye-Tracking Videos.

Sensors (Basel, Switzerland)·2024
查看所有相关文章

相关实验视频

Updated: Jul 3, 2025

Author Spotlight: Enhancing Neurorehabilitation Through EEG, Motor Imagery, and Virtual Reality
10:14

Author Spotlight: Enhancing Neurorehabilitation Through EEG, Motor Imagery, and Virtual Reality

Published on: May 10, 2024

976

根据IMU数据,使用XGBoost对摩托车驾驶者的行为进行分类.

Gerhard Navratil1, Ioannis Giannopoulos1

  • 1Department for Geodesy and Geoinformation, TU Wien, Wiedner Hauptstr. 8-10, 1040 Vienna, Austria.

Sensors (Basel, Switzerland)
|February 10, 2024
PubMed
概括

这项研究表明,惯性测量单元 (IMU) 数据可以以80%的准确度对摩托车驾驶者的行为进行分类,从而帮助环境分析. 超车是唯一的例外,难以可靠地检测.

科学领域:

  • * 人与计算机的交互
  • * * 运输工程 运输工程
  • * 数据科学是一门数据科学.

背景情况:

  • *在航行过程中监测人类行为,可以了解环境条件.
  • *摩托车手需要仔细观察路面和交通,使他们的行为成为关键指标.
  • * 空间和时间分析从了解运动模式中获益.

研究的目的:

  • * 评估惯性测量单元 (IMU) 数据对于分类摩托车驾驶者的行为是否足够.
  • * 探索IMU数据对道路环境的空间和时间分析的潜力.
  • * 评估机器学习模型在行为分类中的有效性.

主要方法:

  • *使用惯性测量单元 (IMU) 传感器进行了一项实验,以收集摩托车手的数据.
  • * XGBoost 机器学习算法用于行为分类.
  • * 数据分析的重点是识别驾驶过程中的不同摩托车手行为.

主要成果:

  • * XGBoost模型成功地分类了五种不同的摩托车驾驶者行为中的四种.
  • * 总体分类准确度达到了大约80%.
  • * 超过机动被确定为使用IMU数据可靠地分类具有挑战性.
关键词:
在IMU,IMU是IMU.在XGBoost中使用.行为分类行为分类.实验 实验 实验 实验 实验摩托车 摩托车 摩托车导航 导航 导航 导航 导航交通 交通 交通 交通 交通

更多相关视频

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

1.4K
A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants
11:14

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

Published on: October 4, 2015

10.9K

相关实验视频

Last Updated: Jul 3, 2025

Author Spotlight: Enhancing Neurorehabilitation Through EEG, Motor Imagery, and Virtual Reality
10:14

Author Spotlight: Enhancing Neurorehabilitation Through EEG, Motor Imagery, and Virtual Reality

Published on: May 10, 2024

976
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

1.4K
A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants
11:14

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

Published on: October 4, 2015

10.9K

结论:

  • *IMU数据是对摩托车驾驶者的行为进行分类的可行来源,对大多数行动具有很高的准确性.
  • * 这种分类能够对道路环境进行有价值的空间和时间分析.
  • *需要进一步的研究,以改善检测复杂的机动,如超车.