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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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相关实验视频

Updated: Jun 9, 2025

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

Published on: July 24, 2019

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使用自拍-视频自动识别面部滴答.

Yocheved Loewenstern, Noa Benaroya-Milshtein, Katya Belelovsky

    IEEE journal of biomedical and health informatics
    |October 30, 2024
    PubMed
    概括
    此摘要是机器生成的。

    这项研究开发了一种客观的智能手机工具,可以自动检测患有滴答障碍的儿童和青少年的面部运动滴答. 人工智能模型的准确度超过90%,为持续的ICT评估提供了一个有前途的方法.

    更多相关视频

    Protocol for Data Collection and Analysis Applied to Automated Facial Expression Analysis Technology and Temporal Analysis for Sensory Evaluation
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    Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
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    相关实验视频

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

    • 神经科学是一个神经科学.
    • 生物医学工程 生物医学工程
    • 计算精神病学是一种计算精神病学.

    背景情况:

    • 滴疾病呈现出不同的症状,使临床评估复杂化.
    • 目前的ICT评估依赖于主观的,不频繁的问卷.
    • 需要客观,持续地监测的表达.

    研究的目的:

    • 开发一种自动化的客观方法来测量面部运动特征.
    • 利用智能手机技术在自然环境中进行tic评估.
    • 改善提克障碍的诊断,监测和治疗评估.

    主要方法:

    • 一个定制的智能手机应用程序记录了参与者的自拍视频.
    • 面部地标被用来从视频段中提取与滴滴相关的特征.
    • 深度神经网络分析了空间和时间特征,用于提克分类.

    主要成果:

    • 开发的模型在所有科目中实现了95%的平均准确性.
    • 交叉验证方案 (离开一个会议,离开一个主题) 的准确度超过了90%.
    • 该系统在识别tic表达式方面表现出了强大的性能.

    结论:

    • 这种自动标识系统提供了一种有价值的客观临床工具.
    • 它有助于诊断,患者随访和治疗疗效评估.
    • 与智能手机技术的整合可以改变临床研究和干预开发.