Creating an artificial intelligence therapy-to-data feedback loop for child developmental healthcare

项目摘要 需要护理的自闭症儿童数量与需要护理的自闭症儿童数量之间存在急剧且日益严重的不平衡 是否有经认证的专家来治疗这种多方面表现的疾病。自闭症社区 面临双重临床挑战：如何引导稀缺的专家资源为多样化的 表型组以及如何监测和验证治疗中的最佳做法。临床医生现在必须寻找解决方案 以分散的方式扩展，将数据捕获、远程监控和治疗越来越多地放在 家庭之手。使用人工智能(AI)和大量标记的人类情感计算机视觉 数据，我们已经开发了一种在谷歌眼镜上运行的自动面部表情识别解决方案 和Android智能手机，向儿童自然状态下的自闭症患者提供实时社交提示 环境。我们假设这个信息系统可以提供实时治疗，这种方式可以扩展到 满足日益增长的自闭症家庭人口的需求，包括未得到充分服务的少数群体 可以用来衡量随着时间的推移和在新型人工智能发展中取得的进展的数据。 我们的第一个目标将集中在开发一种深度学习模型，该模型能够实现动态情感识别 在现实世界中，以及允许最小限度地手动标记以个性化的领域适配过程 对于孩子在家中最经常与之互动的个人，建立最佳准确性的模型。我们的 第二个目标将集中在人机界面上，即设计用户体验与 在谷歌眼镜可穿戴设备上运行的控制会话的Android应用程序。我们会让我们的临床 与同事和自闭症家庭团体一起开发和增强一套游戏和活动模式， 创建情绪治疗的理想社交活动，包括情绪捕捉和字谜游戏。这个 第三个目标将测试我们的中心假设，即眼镜系统可以创建一个治疗到数据反馈循环， 提供临床护理，同时为测量和模型开发提供越来越多的数据。 我们将与多达200名4-8岁的儿童合作，他们最近被诊断为自闭症，但无法接触到 标准行为疗法。我们将通过众包技术建立一个自闭症家庭社区， 符合我们工作中体现的移动范式，并通过与行为疗法的密切合作 提供者、自闭症外展组织自闭症演讲和数字医疗保健公司Cognoa。 这些家庭将与我们合作，设计和改进我们的超强玻璃系统，以适应、接洽、 以及治疗和数据捕获的使用功能。重要的是，我们将把单位和家庭一起送回家使用 该设备每周至少进行3次20分钟的治疗，持续至少6周。这段遥远的时期将 生成一个庞大的数据库，以量化整体社交学习、情感理解、眼神交流和 持续的社交敏锐性。总而言之，我们的工作计划将展示移动可穿戴人工智能可以带来社交学习 过程走出临床，进入真实世界，以获得更快和更适应的干预。

Project Summary There is a sharp and increasing imbalance between the number of children with autism in need of care and the availability of specialists certified to treat the disorder in its multi-faceted manifestations. The autism community faces a dual clinical challenge: how to direct scarce specialist resources to service the diverse array of phenomes and how to monitor and validate best practices in treatment. Clinicians must now look to solutions that scale in a decentralized fashion, placing data capture, remote monitoring, and therapy increasingly into the hands of families. Using artificial intelligence (AI) and large amounts of labeled human emotion computer vision data, we have developed a solution for automatic facial expression recognition that runs on Google Glasses and Android smartphones to deliver real time social cues to individuals with autism in the child’s natural environment. We hypothesize that this informatic system can provide real-time therapy in a way that scales to meet the demand of the growing population of autism families, including underserved minorities, while growing data that can be used to measure progress over time and in the development of novel AI. Our first aim will focus on the development of a deep learning model that enables dynamic emotion recognition in the real world, and on domain adaptation procedures that enable minimal manual labeling to personalize the model for optimal accuracy on the individuals with whom the child will interact most regularly at home. Our second aim will focus on the human computer interface, namely the design of the user experience with the Android application that controls the sessions run on the Google Glass wearable. We will work our clinical colleagues and with groups of autism families to develop and enhance a set of games and activity modes that create social engagements ideal for emotion therapy, including an emotion capture and a charades game. The third aim will test our central hypothesis that the Glass system can create a therapy-to-data feedback loop that delivers clinical care while growing data for measurement and model development. We will work with up to 200 children ages 4-8 who have recent autism diagnoses and do not have access to standard behavioral therapy. We will build a community of autism families through crowdsourcing techniques, befitting the mobile paradigm embodied by our work, and through close collaboration with behavioral therapy providers, the autism outreach organization Autism Speaks, and the digital healthcare company, Cognoa. The families will work with us on design and refinement of our “Superpower Glass” system for fit, engagement, and function of use for both therapy and data capture. Importantly, we will send units home with families to use the device for at least 3 twenty-minute sessions per week for a minimum of 6 weeks. This remote period will generate a massive database to quantify overall social learning, emotion comprehension, eye contact, and sustained social acuity. In all, our work program will show that mobile wearable AI can bring the social learning process out of the clinic and into the real world for faster and more adaptive intervention.