The Performance of Emotion Classifiers for Children With Parent-Reported Autism: Quantitative Feasibility Study
The Performance of Emotion Classifiers for Children With Parent-Reported Autism: Quantitative Feasibility Study
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DOI：
10.2196/13174
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发表时间：
2020-04-01
影响因子：
5.2
通讯作者：
Wall, Dennis Paul
中科院分区：
文献类型：
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作者：
Kalantarian, Haik;Jedoui, Khaled;Wall, Dennis Paul
背景资料：自闭症谱系障碍（ASD）是一种发育障碍，其特征是社会沟通和互动的缺陷，以及限制和重复的行为和兴趣。ASD的发病率近年来有所增加;据估计，美国约有40名儿童中就有1名受到影响。部分由于发病率上升，获得治疗的机会受到限制。希望在于通过人工智能（AI）方法提供治疗的移动的解决方案，包括由主流云提供商开发的面部和情感检测AI模型，直接提供给消费者。然而，这些解决方案可能没有足够的训练，用于儿科population.Objective：情绪分类器提供现成的一般公众通过微软，亚马逊，谷歌，和Sighthound是非常适合的儿科人群，并可用于开发移动的治疗针对社会沟通和互动方面，也许加速在这个空间的创新。本研究的目的是测试这些分类器直接与图像数据，从儿童与父母报告的ASD通过crowdsourcing.Methods招募：我们使用了一个移动的游戏叫猜猜看？这是一个挑战，让孩子们表演出一系列的提示，这些提示显示在他或她的护理提供者的额头上的智能手机屏幕上。该游戏旨在成为孩子和父母进行社交互动的有趣和吸引人的方式，例如，父母试图猜测孩子表现出的情绪（例如，惊讶，害怕或厌恶）。在一个90秒的游戏环节中，当孩子行动时会显示多达50个提示，视频记录了孩子的动作和表情。部分由于游戏的乐趣，这是一种可行的方式来远程参与儿科人群，包括自闭症人群通过众包。我们招募了21名ASD儿童玩游戏，并在他们的游戏会话后收集了2602个情绪帧。这些数据被用来评估四个国家的最先进的面部情绪分类的准确性和性能，以发展这些平台的可行性为pediatric research.Results的理解：所有分类器的表现不佳，除了高兴的每一个评价的情绪。没有一个分类器正确标记了超过60.18%（1566/2602）的评估帧。此外，没有一个分类器正确识别超过11%（6/51）的愤怒帧和14%（10/69）的厌恶frames.Conclusions：研究结果表明，商业情绪分类器可能没有足够的训练，用于数字方法自闭症治疗和治疗跟踪。需要安全、隐私保护的方法来增加标记的训练数据，以提高模型的性能，然后才能将其用于自闭症治疗中常见的AI社交治疗方法。
Background: Autism spectrum disorder (ASD) is a developmental disorder characterized by deficits in social communication and interaction, and restricted and repetitive behaviors and interests. The incidence of ASD has increased in recent years; it is now estimated that approximately 1 in 40 children in the United States are affected. Due in part to increasing prevalence, access to treatment has become constrained. Hope lies in mobile solutions that provide therapy through artificial intelligence (AI) approaches, including facial and emotion detection AI models developed by mainstream cloud providers, available directly to consumers. However, these solutions may not be sufficiently trained for use in pediatric populations.Objective: Emotion classifiers available off-the-shelf to the general public through Microsoft, Amazon, Google, and Sighthound are well-suited to the pediatric population, and could be used for developing mobile therapies targeting aspects of social communication and interaction, perhaps accelerating innovation in this space. This study aimed to test these classifiers directly with image data from children with parent-reported ASD recruited through crowdsourcing.Methods: We used a mobile game called Guess What? that challenges a child to act out a series of prompts displayed on the screen of the smartphone held on the forehead of his or her care provider. The game is intended to be a fun and engaging way for the child and parent to interact socially, for example, the parent attempting to guess what emotion the child is acting out (eg, surprised, scared, or disgusted). During a 90-second game session, as many as 50 prompts are shown while the child acts, and the video records the actions and expressions of the child. Due in part to the fun nature of the game, it is a viable way to remotely engage pediatric populations, including the autism population through crowdsourcing. We recruited 21 children with ASD to play the game and gathered 2602 emotive frames following their game sessions. These data were used to evaluate the accuracy and performance of four state-of-the-art facial emotion classifiers to develop an understanding of the feasibility of these platforms for pediatric research.Results: All classifiers performed poorly for every evaluated emotion except happy. None of the classifiers correctly labeled over 60.18% (1566/2602) of the evaluated frames. Moreover, none of the classifiers correctly identified more than 11% (6/51) of the angry frames and 14% (10/69) of the disgust frames.Conclusions: The findings suggest that commercial emotion classifiers may be insufficiently trained for use in digital approaches to autism treatment and treatment tracking. Secure, privacy-preserving methods to increase labeled training data are needed to boost the models' performance before they can be used in AI-enabled approaches to social therapy of the kind that is common in autism treatments.