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相关概念视频

Force Classification01:22

Force Classification

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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,...
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Classification of Systems-I01:26

Classification of Systems-I

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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:
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Classification of Signals01:30

Classification of Signals

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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...
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Classification of Skeletal Muscle Fibers01:48

Classification of Skeletal Muscle Fibers

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Skeletal muscles continuously produce ATP to provide the energy that enables muscle contractions. Skeletal muscle fibers can be categorized into three types based on differences in their contraction speed and how they produce ATP, as well as physical differences related to these factors. Most human muscles contain all three muscle fiber types, albeit in varying proportions.
Slow-Twitch Muscle Fibers
Slow oxidative, muscle fibers appear red due to large numbers of capillaries and high levels of...
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Aggregates Classification01:29

Aggregates Classification

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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...
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Classification of Systems-II01:31

Classification of Systems-II

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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,
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Deep Neural Networks for Image-Based Dietary Assessment
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在集体学习中使用卷积神经网络进行训练分类.

Gi-Seung Bang1, Seung-Bo Park1

  • 1Department of Software Convergence Engineering, Inha University, Incheon 22212, Republic of Korea.

Sensors (Basel, Switzerland)
|May 25, 2024
PubMed
概括
此摘要是机器生成的。

本研究引入了使用卷积神经网络 (CNN) 和集体学习的实时运动姿势分类系统. 该系统在各种练习中实现了高精度,有助于个性化的健身和物理治疗.

关键词:
媒体管道 (MediaPipe) 是一个媒体管道.计算机视觉 计算机视觉卷积神经网络是一种卷积神经网络.组合学习组合学习家庭炼 在家炼

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科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能
  • 生物医学工程 生物医学工程

背景情况:

  • 随着COVID-19的流行,人们对家庭炼解决方案的需求增加了.
  • 准确的实时运动姿势分类对于有效的远程健身指导至关重要.

研究的目的:

  • 开发一种新的实时运动姿势分类系统.
  • 通过集体学习和CNN来提高分类准确性.

主要方法:

  • 使用的MediaPipe用于人体关节坐标和角度提取.
  • 使用卷积神经网络 (CNN) 进行模式识别.
  • 实施了一种集体学习方法,将多个的预测结合起来.

主要成果:

  • 在健身基本数据集上实现了高精度 (92.12%),精度 (91.62%),回忆 (91.64%) 和F1得分 (91.58%).
  • 成功分类练习,包括手臂抬起,,和头部按压实时.

结论:

  • 拟议的系统证明了有效的实时运动姿势分类.
  • 潜在的应用包括个性化的健身建议和物理治疗服务.