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

MALDI-TOF Mass Spectrometry01:19

MALDI-TOF Mass Spectrometry

Mass spectrometry is a powerful characterization technique that can identify and separate a wide variety of compounds ranging from chemical to biological entities, based on their mass-to-charge ratio (m/z). The instruments that allow this detection, known as mass spectrometers, have three components: an ion source, a mass analyzer, and a detector. These spectrometers differ based on the nature of their ion source and analyzers.Matrix-assisted laser desorption ionization (MALDI) is a commonly...
Proteomics01:33

Proteomics

A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term proteomics...
Microbial Biosensors01:17

Microbial Biosensors

Microbial biosensors are analytical devices that utilize living microbes to detect specific substances through measurable signals. These devices consist of two main components: biosensing organisms and signal-transducing elements. Biosensing organisms, such as Escherichia coli or Saccharomyces cerevisiae, are typically housed in multiwell plates connected to transducers, enabling rapid, real-time detection of target analytes.Signal Generation MechanismWhen a target analyte—such as...

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

K<sup>+</sup>/Pb<sup>2+</sup> ion exchange-induced structural transitions of G-quadruplexes under molecular crowding conditions.

RSC advances·2026
Same author

Mapping 3D genome organization at nucleosome-scale with Micro-C and Region Capture Micro-C (RCMC).

Nature protocols·2026
Same author

Ion-structured transcriptional network reorganization is associated with tissue-specific adaptation to saline-alkaline stress in sorghum.

BMC plant biology·2026
Same author

Changing patterns and related factors of kimchi consumption among Korean adults: a nationwide cross-sectional analysis of the Korea National Health and Nutrition Examination Survey, 2010-2024.

Korean journal of community nutrition·2026
Same author

Brain barriers as checkpoints in endocrine regulation of body homeostasis.

Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism·2026
Same author

Remodeling TME via feedback-driven photothermal-ferroptosis-immune cascade.

Biomaterials·2026

相关实验视频

Updated: Jul 8, 2026

Evaluation of Commercial-Off-The-Shelf Wrist Wearables to Estimate Stress on Students
12:51

Evaluation of Commercial-Off-The-Shelf Wrist Wearables to Estimate Stress on Students

Published on: June 16, 2018

7.6K

机器学习用于从可穿戴设备中分析生物信号.

Inhea Jeong1,2, Won Gi Chung1,2, Enji Kim1,2

  • 1Department of Materials Science and Engineering, Yonsei University, Seoul 03722, Republic of Korea. jang-ung@yonsei.ac.kr.

Materials horizons
|May 29, 2025
PubMed
概括

机器学习 (ML) 通过改进生物信号分析来增强可穿戴生物电子设备的实时健康监测. 本综述指导选择ML模型以从复杂数据中获得准确的健康见解.

更多相关视频

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
10:28

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published on: July 24, 2019

15.4K
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

4.2K

相关实验视频

Last Updated: Jul 8, 2026

Evaluation of Commercial-Off-The-Shelf Wrist Wearables to Estimate Stress on Students
12:51

Evaluation of Commercial-Off-The-Shelf Wrist Wearables to Estimate Stress on Students

Published on: June 16, 2018

7.6K
Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
10:28

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published on: July 24, 2019

15.4K
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

4.2K

科学领域:

  • 生物医学工程 生物医学工程
  • 数据科学数据科学数据科学
  • 医疗信息学 医疗信息学

背景情况:

  • 可穿戴生物电子设备可实现持续的健康监测和个性化洞察力.
  • 生物信号数据由于体积,复杂性,噪声和工件而带来了挑战.
  • 机器学习 (ML) 对于处理复杂的生物信号数据和发现模式至关重要.

研究的目的:

  • 审查生物信号处理的关键ML算法.
  • 为选择合适的ML模型提供准则.
  • 讨论ML在健康监测和疾病预测中的应用.

主要方法:

  • 探索用于生物信号处理的ML算法.
  • 讨论数据预处理技术.
  • 审查ML模型,包括集群,回归和分类.
  • 对基于机器学习的分析的评估方法的检查.

主要成果:

  • 确定ML模型选择的关键因素:数据特征,处理目标,计算效率和准确性.
  • 跨神经,心血管和生化生物信号的ML应用概述.
  • 突出 ML 与可穿戴生物电子设备的整合,以实现先进的健康监测.

结论:

  • 机器学习对于克服分析可穿戴设备复杂生物信号数据的挑战至关重要.
  • 仔细的模型选择和预处理是准确的ML驱动生物信号分析的关键.
  • 机器学习与可穿戴生物电子技术的整合有望彻底改变医疗保健系统.