遇见数据集

TASD-Dataset: Text-based Early Autism Spectrum Disorder Detection Dataset for Toddlers

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DataCite Commons2025-05-01 更新2025-04-16 收录
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The TASD-Dataset is tailored for early ASD detection in toddlers, providing detailed textual sequences that describe the daily situations of toddlers both with and without ASD. It incorporates essential ASD assessment features such as Attention Response, Word Repetition, Emotional Empathy, and introduces new features including Noise Sensitivity, Sharing Interest, Sign Communication, and Tiptoe Flapping. Each feature is intricately linked with specific toddler behaviors, offering nuanced insights for parents and aiding in the identification of ASD-related signals. By emphasizing these features, the dataset facilitates the development of machine learning models to explore behavioral markers crucial for early ASD risk detection. Here's a summary of the key features: - Attention Response: Reflects how toddlers respond to external stimuli, indicating differences in sensory processing or responsiveness. - Change Reaction: Assesses how toddlers adapt to changes, potentially indicating challenges in flexibility or coping mechanisms. - Word Repetition: Involves repetitive use of words or phrases, often seen in children with ASD as echolalia or repetitive speech. - Eye Contact: Indicates the frequency and quality of a child's eye contact during social interactions, impacting social communication abilities. - Emotional Empathy: Measures a child's ability to comprehend and respond to others' emotions. - Finger Movements: Observes repetitive finger movements or hand gestures, often indicative of self-stimulatory behaviors. - Focused Attention: Evaluates a child's capacity to maintain concentration on a task, potentially indicating attention deficits. - Follow Pointing: Assesses a child's ability to follow pointing gestures, a skill crucial for social communication. - Repetitive Behavior: Encompasses persistent and ritualistic actions or interests commonly seen in ASD. - Toy Arranging: Examines a child's inclination or patterns in arranging toys, reflecting preferences or behaviors seen in ASD. - Noise Sensitivity: Gauges a toddler's sensitivity to auditory stimuli, including reactions to loud noises or certain frequencies. - Sharing Interest: Assesses a child's ability to engage in joint attention and reciprocal interactions with others. - Sign Communication: Evaluates the use of gestures or signs as a means of communication, crucial for early communication skills. - Tiptoe Flapping: Observes repetitive behaviors such as tiptoe walking combined with hand flapping, potentially associated with sensory-seeking behaviors seen in ASD. The dataset for Early ASD Detection in Toddlers offers valuable insights into behavioral features associated with ASD, providing researchers and practitioners with a rich resource for advancing early intervention strategies and improving outcomes for children with ASD.

TASD数据集(TASD-Dataset)专为幼儿孤独症谱系障碍(ASD)早期筛查打造,提供了详细的文本序列,用以描述患有与未患有孤独症谱系障碍的幼儿的日常场景。该数据集纳入了孤独症谱系障碍评估的核心特征,如注意反应、词语重复、情感共情,并新增了噪音敏感度、共同兴趣分享、手势沟通与踮脚拍手等特征。每一项特征均与幼儿的特定行为紧密关联,可为家长提供精细化的洞察视角,助力识别与孤独症谱系障碍相关的行为信号。通过聚焦上述特征,该数据集可助力机器学习模型的开发,以探索对孤独症谱系障碍早期风险筛查至关重要的行为标记物。 以下为核心特征概述: - 注意反应:反映幼儿对外部刺激的回应方式,可体现其感觉处理或响应性的差异。 - 变化反应:评估幼儿适应变化的能力,可体现其在灵活性或应对机制方面可能存在的困难。 - 词语重复:指重复使用词语或短语的行为,孤独症谱系障碍儿童常出现此类模仿言语或重复性言语表现。 - 眼神交流:体现幼儿在社交互动中的眼神交流频率与质量,对社交沟通能力具有重要影响。 - 情感共情:衡量幼儿理解并回应他人情绪的能力。 - 手指动作:观测幼儿重复性手指动作或手部手势,常为自我刺激行为的典型表现。 - 专注注意力:评估幼儿维持任务专注力的能力,可体现其可能存在的注意力缺陷。 - 跟随指物:评估幼儿跟随指物手势的能力,该技能对社交沟通至关重要。 - 重复性行为:涵盖孤独症谱系障碍儿童常见的持续性、仪式化动作或兴趣偏好。 - 玩具摆放:考察幼儿摆放玩具的倾向与模式,可体现其孤独症谱系障碍相关的偏好或行为特征。 - 噪音敏感度:衡量幼儿对听觉刺激的敏感程度,包括对巨响或特定频率声音的反应。 - 共同兴趣分享:评估幼儿与他人开展共同注意与双向互动的能力。 - 手势沟通:评估幼儿以手势或符号作为沟通方式的能力,对早期沟通技能的发展至关重要。 - 踮脚拍手:观测踮脚行走结合手部拍动的重复性行为,此类表现常与孤独症谱系障碍儿童的感觉寻求行为相关。 本幼儿孤独症谱系障碍早期筛查数据集可为孤独症谱系障碍相关行为特征提供极具价值的洞察视角,为研究人员与临床从业者提供了丰富的资源,助力推进早期干预策略并改善孤独症谱系障碍儿童的预后效果。

提供机构:
Mendeley Data
创建时间:
2023-12-12
搜集汇总
背景与挑战
背景概述
TASD-Dataset是一个专为幼儿早期自闭症谱系障碍(ASD)检测设计的文本数据集,包含描述ASD和非ASD幼儿日常行为的详细文本序列。数据集整合了注意力反应、重复言语、情感共情等关键ASD评估特征,并新增了噪声敏感性、兴趣共享等创新特征,每个特征均关联具体幼儿行为,为开发早期ASD风险检测的机器学习模型提供支持。
以上内容由遇见数据集搜集并总结生成
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