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相关概念视频

Muscles for Facial Expressions01:14

Muscles for Facial Expressions

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The craniofacial muscles are a collection of approximately 20 thin skeletal muscles situated beneath the skin of the face and scalp. These muscles, primarily responsible for the vast array of human facial expressions, originate from the bones or fibrous structures of the skull and extend outwards to connect with the skin. While most skeletal muscles in the body are enveloped in thick fascia, facial muscles generally have a more delicate fascial covering, with the buccinator muscle being a...
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Assessment of Airway, Skin Color, and Use of Accessory Muscles01:30

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A thorough assessment of respiratory health is paramount in clinical settings to identify and manage respiratory distress and ensure adequate oxygenation. This article elaborates on the critical aspects of respiratory evaluation, including airway assessment, skin color examination, and the observation of accessory muscle use, which are integral to effectively diagnosing and managing patients with respiratory conditions.
Introduction
The initial evaluation of a patient's respiratory system...
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Facial Feedback Hypothesis01:24

Facial Feedback Hypothesis

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Charles Darwin proposed that facial expressions are an evolutionary adaptation for communication. He argued that these expressions are not influenced by culture but are universal across species. For example, a snarling expression with exposed teeth signals a threat in many animals, including humans. Darwin also suggested that displaying an emotion can intensify the feeling. Smiling, for example, could enhance one's sense of happiness. This idea laid the foundation for understanding the role...
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Therapeutic Communication01:30

Therapeutic Communication

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Communication is a lifelong learning process. Through therapeutic communication, nurses can collect relevant assessment data, provide education and counseling, and interact during nursing interventions. Sending and receiving messages occur through verbal and nonverbal communication techniques and can happen separately or simultaneously.
Verbal communication depends on language or a prescribed way of using words so that people can share information effectively. The critical aspects of verbal...
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相关实验视频

Updated: Feb 28, 2026

A Protocol for Comprehensive Assessment of Bulbar Dysfunction in Amyotrophic Lateral Sclerosis ALS
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A Protocol for Comprehensive Assessment of Bulbar Dysfunction in Amyotrophic Lateral Sclerosis ALS

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面部表情注释和分析对于失联症严重程度分类的分析.

Shufei Duan1,2, Yuxin Guo2, Longhao Fu2

  • 1College of Computer Science and Technology, Shanxi University of Electronic Science and Technology, Linfen 041000, China.

Sensors (Basel, Switzerland)
|February 27, 2026
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的方法,通过结合面部和声学数据来评估中风后性肌痛. 这种多式联络方法提高了分类语音障碍严重程度的准确性.

关键词:
德劳内三角形测定方法脱节性关节障碍症 脱节性关节障碍症是一种疾病.面部动作单位 面部动作单位脸部的视觉特征补偿图表 卷积网络 卷积网络关键框架标签的标签

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

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Protocol for Data Collection and Analysis Applied to Automated Facial Expression Analysis Technology and Temporal Analysis for Sensory Evaluation
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Protocol for Data Collection and Analysis Applied to Automated Facial Expression Analysis Technology and Temporal Analysis for Sensory Evaluation

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相关实验视频

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Published on: February 21, 2011

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

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Protocol for Data Collection and Analysis Applied to Automated Facial Expression Analysis Technology and Temporal Analysis for Sensory Evaluation
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科学领域:

  • 神经学 神经学
  • 语音语言病理学 语言病理学
  • 计算机科学 计算机科学

背景情况:

  • 脑卒中后的肌痛性关节障碍通常涉及中部面部,影响运动控制和情绪表达.
  • 目前的评估主要使用声学数据,忽视了关键的视觉面部线索及其情感相关性.
  • 这种局限性阻碍了全面的疾病评估和对言语障碍的理解.

研究的目的:

  • 开发一个综合面部和声学特征的多式重症分类框架,用于脑卒中后性肌痛.
  • 为了克服数据稀缺问题,使用多级注释算法.
  • 加强语言障碍评估的客观评估和临床应用.

主要方法:

  • 开发了一种使用预训练模型和运动幅度的多层次注释算法.
  • 用Delaunay三角化和通过图形卷积网络 (GCNs) 捕获的空间关系来建模面部拓.
  • 使用面部动作单位 (AU) 量化异常肌肉协调,并提出了多式特征融合框架.

主要成果:

  • 拟议的多式联运框架在THE-POSSD数据集上实现了92.0%的准确性和91.6%的F1得分.
  • 性能明显超过单一模式基线方法.
  • 该研究确定了患者面部运动和与情绪状态相关的敏感区域的变化.

结论:

  • 多式模式框架有效地弥补了声学模式的局限性与视觉面部特征.
  • 它表明对语言障碍的客观评估有很大的潜力.
  • 这种方法为未来的临床应用提供了有希望的途径,用于评估中风后患者.