使用特定领域人工智能改进发育儿科学的数字疗法：机器学习研究
Improved Digital Therapy for Developmental Pediatrics Using Domain-Specific Artificial Intelligence: Machine Learning Study
AI总结：
本研究通过游戏化策略收集和标记儿童情绪数据，构建了比现有数据集大30倍的儿科情绪数据库，训练的CNN分类器在CAFE数据集上性能提升超10%，为发育行为障碍儿童的数字疗法提供了更优的情绪识别方案。
摘要：
背景：自动化情绪分类可以帮助那些难以识别情绪的人，包括患有自闭症等发育行为障碍的儿童。然而，大多数计算机视觉情绪识别模型是在成人情绪数据上训练的，因此在应用于儿童面部时表现不佳。目的：我们设计了一种将儿童情绪丰富图像的收集和标记游戏化的策略，以将自动儿童情绪识别模型的性能提升到更接近数字医疗方法所需的水平。方法：我们利用原型治疗智能手机游戏GuessWhat（主要为发育和行为障碍儿童设计），将游戏提示儿童表达各种情绪的视频数据安全收集过程游戏化。同时，我们独立创建了一个安全的Web界面，将人工标记工作游戏化，称为HollywoodSquares，适合任何合格的标记员使用。我们收集并标记了2155个视频、39,968个情绪帧和106,001个图像标签。利用这个大幅扩展的以儿科情绪为中心的数据库（比现有公共儿科情绪数据集大30倍以上），我们训练了一个卷积神经网络（CNN）计算机视觉分类器，用于识别儿童引发的快乐、悲伤、惊讶、恐惧、愤怒、厌恶和中性表情。结果：该分类器在整个Child Affective Facial Expression (CAFE)数据集上实现了66.9%的平衡准确率和67.4%的F1分数，在CAFE Subset A（包含至少60%人类对情绪标签一致性的子集）上实现了79.1%的平衡准确率和78%的F1分数。这一性能比之前所有针对CAFE评估的分类器高出至少10%，其中最好的分类器即使将“愤怒”和“厌恶”合并为单一类别，也仅达到56%的平衡准确率。
英文摘要：
Background: Automated emotion classification could aid those who struggle to recognize emotions, including children with developmental behavioral conditions such as autism. However, most computer vision emotion recognition models are trained on adult emotion and therefore underperform when applied to child faces. Objective: We designed a strategy to gamify the collection and labeling of child emotion-enriched images to boost the performance of automatic child emotion recognition models to a level closer to what will be needed for digital health care approaches. Methods: We leveraged our prototype therapeutic smartphone game, GuessWhat, which was designed in large part for children with developmental and behavioral conditions, to gamify the secure collection of video data of children expressing a variety of emotions prompted by the game. Independently, we created a secure web interface to gamify the human labeling effort, called HollywoodSquares, tailored for use by any qualified labeler. We gathered and labeled 2155 videos, 39,968 emotion frames, and 106,001 labels on all images. With this drastically expanded pediatric emotion-centric database (>30 times larger than existing public pediatric emotion data sets), we trained a convolutional neural network (CNN) computer vision classifier of happy, sad, surprised, fearful, angry, disgust, and neutral expressions evoked by children. Results: The classifier achieved a 66.9% balanced accuracy and 67.4% F1-score on the entirety of the Child Affective Facial Expression (CAFE) as well as a 79.1% balanced accuracy and 78% F1-score on CAFE Subset A, a subset containing at least 60% human agreement on emotions labels. This performance is at least 10% higher than all previously developed classifiers evaluated against CAFE, the best of which reached a 56% balanced accuracy even when combining "anger" and "disgust" into a single class.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
