Collaborative Research: SCH: Multimodal Algorithms for Motor Imitation Assessment in Children with Autism

合作研究：SCH：自闭症儿童运动模仿评估的多模式算法

基本信息

批准号：
2124277
负责人：
Rene Vidal
金额：
$ 65.91万
依托单位：
Johns Hopkins University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-10-01 至 2025-09-30
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2124277&HistoricalAwards=false
关键词：
Collaborative Research SCH Multimodal Algorithms

项目摘要

Approximately 1 in 54 children in the US is diagnosed with autism spectrum disorder (ASD). Given its high prevalence, there is a need for an automatic and scalable method to inform diagnosis and behavioral therapies. While prior work on finding early-emerging and reliable quantitative biomarkers of ASD has focused on non-motor features, abundant research evidence has revealed patterns of impaired motor imitation in a wide range of children with ASD, making motor imitation deficits a promising avenue to find a phenotypic biomarker. However, traditional imitation assessment methods often rely on expert-based observation, which is costly, time-consuming and error-prone, and lacks objectivity and scalability. Recent advances in computer vision and machine learning make artificial intelligence a promising technology to design an objective, reproducible and highly-scalable multimodal system functioning not only in well-equipped clinical setups but also at home for assessing imitation performance in children with ASD. However, critical challenges such as the design of specific imitation tasks for ASD assessment, the collection and labeling of multimodal data for training machine learning algorithms, and the development of novel fine-grained representations human movements and metrics for comparing such movements need to be addressed to test the validity, scalability and reproducibility of automatic motor imitation assessment algorithms to inform ASD diagnosis.The overall goal of this project is to design, develop and test an objective, reproducible and highly-scalable multimodal system to observe children performing a brief video game-like motor imitation task, quantitatively assess their motor imitation performance, and investigate its validity as a phenotypic biomarker for autism. Accomplishing this goal will require an interdisciplinary approach which combines expertise in autism, child development, computer vision and machine learning. Specifically, this project will: (1) design motor imitation tasks that are relevant for ASD assessment, (2) design, test and validate a scalable and flexible system to collect and label multimodal data of children imitating a sequence of movements; (3) design a novel fine-grained representation of human movements that can be learned efficiently and is suitable for comparing the children's movements to the movements they need to imitate; (4) develop novel computer vision and metric learning algorithms for learning and comparing multimodal representations of human movements, and (5) use such metrics to generate candidate imitations scores that can be used as potential quantitative biomarkers for ASD. The motor imitation assessment methods to be developed in this project could be used in a wide variety of applications beyond assessing children with ASD, such as providing imitation performance scores for video-based rehabilitation therapy, surgical skill assessment, athletic activities and other movement-based instructional activities.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

在美国，大约有54名儿童被诊断出患有自闭症谱系障碍（ASD）。鉴于其高患病率，需要一种自动且可扩展的方法来告知诊断和行为疗法。尽管ASD的早期出现和可靠的量化生物标志物的先前工作重点是非运动特征，但大量的研究证据揭示了在广泛的ASD儿童中，运动模仿受损的模式，使运动模仿缺陷成为一个有前途的大道，可以找到一个表型生物标志物。但是，传统的模仿评估方法通常依赖于基于专家的观察，这是昂贵的，耗时且容易出错的观察，并且缺乏客观性和可扩展性。计算机视觉和机器学习的最新进展使人工智能成为设计一种有前途的技术，它不仅在设备齐全的临床设置中运作，而且还可以评估ASD儿童的模仿性能。 However, critical challenges such as the design of specific imitation tasks for ASD assessment, the collection and labeling of multimodal data for training machine learning algorithms, and the development of novel fine-grained representations human movements and metrics for comparing such movements need to be addressed to test the validity, scalability and reproducibility of automatic motor imitation assessment algorithms to inform ASD diagnosis.The overall goal of this project is to design, develop and test an客观，可重现且高度可观的多模式系统，可观察儿童执行短暂的视频游戏式仿真任务，定量评估其运动模仿性能，并研究其作为自闭症表型生物标志物的有效性。实现这一目标将需要一种跨学科的方法，该方法结合了自闭症，儿童发展，计算机视觉和机器学习方面的专业知识。具体而言，该项目将：（1）设计与ASD评估相关的电动机模仿任务，（2）设计，测试和验证可扩展且灵活的系统，以收集和标记儿童的多模式数据，模仿了一系列运动；（3）设计一种可以有效学习的人类运动的新型细粒度表示，适合将孩子的运动与他们所需的模仿运动进行比较；（4）开发用于学习和比较人类运动的多模式表示的新型计算机视觉和度量学习算法，并且（5）使用此类指标来生成可用作ASD的潜在定量生物标志物的候选模仿得分。除了评估ASD评估儿童之外，还可以在该项目中开发的运动模仿评估方法，例如为基于视频的康复治疗，手术技能评估，运动活动和其他基于运动的教学活动提供模仿性能得分。该奖项反映了NSF的法定任务，并通过评估范围来反映出范围的Infactia intiftia intiftia intiftia intiftial intiftia intifial intiftial intift and Foundlial and Foundliat and foothial的基础。