Scholarlink

<p>Monitoring behaviors associated with happiness (e.g., smiling, laughing) and unhappiness (e.g., crying, frowning) supports evaluation of an intervention’s social validity and sustainability. This is especially important for children with communication challenges, such as those with autism spectrum disorder (ASD). However, behavioral indicators of emotional states are often subtle, idiosyncratic, and subjective, which complicates data collection and analysis. Technological innovations in machine learning, particularly neural network models, may improve efficiency and accuracy when measuring indices of happiness and unhappiness. This pilot study evaluated the feasibility of using convolutional neural networks (CNNs) and a combined CNN with a dense neural network (CNN + DNN) to detect emotional expressions in young children with ASD. Labeled video recordings from clinical settings were used as ground truth data to train and test the models in a naturalistic context. The CNN + DNN model achieved over 93% on accuracy, precision, recall, and F1 score. Potential implications for applied practice and directions for future research are discussed.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Comparison of Neural Network Methods for Identifying Happiness Indices in Autistic Children

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月30日

查看全文
  • Tobias Kausch,
  • Leslie Neely,
  • Adel Alaeddini,
  • Katie Holloway,
  • Melissa G. Ruiz,
  • Amarie Carnett,
  • Russell Lang

摘要

Monitoring behaviors associated with happiness (e.g., smiling, laughing) and unhappiness (e.g., crying, frowning) supports evaluation of an intervention’s social validity and sustainability. This is especially important for children with communication challenges, such as those with autism spectrum disorder (ASD). However, behavioral indicators of emotional states are often subtle, idiosyncratic, and subjective, which complicates data collection and analysis. Technological innovations in machine learning, particularly neural network models, may improve efficiency and accuracy when measuring indices of happiness and unhappiness. This pilot study evaluated the feasibility of using convolutional neural networks (CNNs) and a combined CNN with a dense neural network (CNN + DNN) to detect emotional expressions in young children with ASD. Labeled video recordings from clinical settings were used as ground truth data to train and test the models in a naturalistic context. The CNN + DNN model achieved over 93% on accuracy, precision, recall, and F1 score. Potential implications for applied practice and directions for future research are discussed.