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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，大约每54名儿童中就有1名被诊断患有自闭症谱系障碍（ASD）。鉴于其高流行率，需要一种自动和可扩展的方法来告知诊断和行为治疗。虽然先前寻找早期出现的和可靠的ASD定量生物标志物的工作主要集中在非运动特征上，但大量的研究证据揭示了广泛的ASD儿童中运动模仿受损的模式，使运动模仿缺陷成为寻找表型生物标志物的有希望的途径。然而，传统的模仿评估方法往往依赖于专家的观察，成本高、耗时长、易出错，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一种很有前途的技术，可以设计一种客观、可重复和高度可扩展的多模态系统，不仅可以在设备齐全的临床设备中使用，还可以在家中评估ASD儿童的模仿能力。然而，关键的挑战，如ASD评估的特定模仿任务的设计，用于训练机器学习算法的多模态数据的收集和标记，以及新的细粒度表示人类运动和用于比较这些运动的度量的开发需要得到解决，以测试有效性，自动运动模仿评估算法的可扩展性和可重复性，以告知ASD诊断。该项目的总体目标是设计，开发和测试一个目标，可重复和高度可扩展的多模式系统，观察儿童执行简短的视频游戏样运动模仿任务，定量评估他们的运动模仿表现，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跨学科的方法，结合自闭症，儿童发展，计算机视觉和机器学习的专业知识。具体而言，本项目将：（1）设计与ASD评估相关的运动模仿任务，（2）设计、测试和验证一个可扩展的灵活系统，以收集和标记儿童模仿一系列运动的多模态数据;（3）设计新颖精细的-人类动作的颗粒化表示，可以有效地学习，并适合于将儿童的动作与他们需要的动作进行比较模仿（4）开发新的计算机视觉和度量学习算法，用于学习和比较人类运动的多模态表示，以及（5）使用这些度量来生成候选模仿分数，这些分数可以用作ASD的潜在定量生物标志物。本项目中开发的运动模仿评估方法可用于评估ASD儿童以外的各种应用，例如为基于视频的康复治疗提供模仿表现评分，手术技能评估，体育活动和其他运动-该奖项反映了NSF的法定使命，并被认为是值得通过使用基金会的知识价值和更广泛的影响审查标准。