你猜怎么着?利用面部表情从结构化视频中理解自闭症。

IF 5.9 Q1 Computer Science Journal of Healthcare Informatics Research Pub Date : 2019-01-01 DOI:10.1007/s41666-018-0034-9
Haik Kalantarian, Peter Washington, Jessey Schwartz, Jena Daniels, Nick Haber, Dennis P Wall
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引用次数: 17

摘要

据估计,美国每59名儿童中就有1名患有自闭症谱系障碍(ASD)。由于诊断的延误和覆盖范围的不平衡,有必要开发新的护理提供方法,通过利用在临床环境之外使用的移动工具和可穿戴设备,适当增强儿童和护理人员的权能。在本文中,我们将呈现一款手机字谜游戏《Guess What?》,用于获取ASD儿童的结构化视频,用于行为疾病研究。然后,我们将面部跟踪和情感识别算法应用于通过Guess What获得的视频。玩游戏。通过分析面部对各种提示的反应,我们证明了参与和面部影响可以使用实时图像处理算法进行量化和测量:这是未来治疗、家庭筛查和基于家庭视频的结果测量的重要第一步。我们对8名受试者的研究证明了该系统通过引人入胜的游戏化移动平台从ASD儿童中获得高度情绪化的结构化视频的有效性,同时揭示了最有效的提示和类别,以产生参与者的不同情绪。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Guess What?: Towards Understanding Autism from Structured Video Using Facial Affect.

Autism Spectrum Disorder (ASD) is a condition affecting an estimated 1 in 59 children in the United States. Due to delays in diagnosis and imbalances in coverage, it is necessary to develop new methods of care delivery that can appropriately empower children and caregivers by capitalizing on mobile tools and wearable devices for use outside of clinical settings. In this paper, we present a mobile charades-style game, Guess What?, used for the acquisition of structured video from children with ASD for behavioral disease research. We then apply face tracking and emotion recognition algorithms to videos acquired through Guess What? game play. By analyzing facial affect in response to various prompts, we demonstrate that engagement and facial affect can be quantified and measured using real-time image processing algorithms: an important first-step for future therapies, at-home screenings, and outcome measures based on home video. Our study of eight subjects demonstrates the efficacy of this system for deriving highly emotive structured video from children with ASD through an engaging gamified mobile platform, while revealing the most efficacious prompts and categories for producing diverse emotion in participants.

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来源期刊
Journal of Healthcare Informatics Research
Journal of Healthcare Informatics Research Computer Science-Computer Science Applications
CiteScore
13.60
自引率
1.70%
发文量
12
期刊介绍: Journal of Healthcare Informatics Research serves as a publication venue for the innovative technical contributions highlighting analytics, systems, and human factors research in healthcare informatics.Journal of Healthcare Informatics Research is concerned with the application of computer science principles, information science principles, information technology, and communication technology to address problems in healthcare, and everyday wellness. Journal of Healthcare Informatics Research highlights the most cutting-edge technical contributions in computing-oriented healthcare informatics.  The journal covers three major tracks: (1) analytics—focuses on data analytics, knowledge discovery, predictive modeling; (2) systems—focuses on building healthcare informatics systems (e.g., architecture, framework, design, engineering, and application); (3) human factors—focuses on understanding users or context, interface design, health behavior, and user studies of healthcare informatics applications.   Topics include but are not limited to: ·         healthcare software architecture, framework, design, and engineering;·         electronic health records·         medical data mining·         predictive modeling·         medical information retrieval·         medical natural language processing·         healthcare information systems·         smart health and connected health·         social media analytics·         mobile healthcare·         medical signal processing·         human factors in healthcare·         usability studies in healthcare·         user-interface design for medical devices and healthcare software·         health service delivery·         health games·         security and privacy in healthcare·         medical recommender system·         healthcare workflow management·         disease profiling and personalized treatment·         visualization of medical data·         intelligent medical devices and sensors·         RFID solutions for healthcare·         healthcare decision analytics and support systems·         epidemiological surveillance systems and intervention modeling·         consumer and clinician health information needs, seeking, sharing, and use·         semantic Web, linked data, and ontology·         collaboration technologies for healthcare·         assistive and adaptive ubiquitous computing technologies·         statistics and quality of medical data·         healthcare delivery in developing countries·         health systems modeling and simulation·         computer-aided diagnosis
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