CAREER: Learning Visual Representations of Motor Function in Infants as Prodromal Signs for Autism

职业：学习婴儿运动功能的视觉表征作为自闭症的前驱症状

• 批准号: 2143882
• 负责人: Sarah Ostadabbas
• 金额: $ 60万
• 依托单位: Northeastern University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-05-01 至 2027-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2143882&HistoricalAwards=false
• 关键词: CAREER; Learning; Visual; Representations; Motor

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).Autism Spectrum Disorder (ASD) is a neurodevelopmental condition that affects social communication and flexible behavior in up to 1.5% of the population. Movement disorders are considered one of the first signs of ASD, and probably precede social or linguistic abnormalities. But differences in motor movement quality are also associated with other conditions, such as Cerebral Palsy and Developmental Coordination Disorder, so while early motor deficits are not in themselves diagnostic of ASD they are risk indicators. This research will directly aid in early identification of motor deficits in infants, thereby enabling early treatment resulting in better quality of life as well as reduced healthcare and education costs. Additional broad impact will derive from inclusion of an underserved, low-income population with health disparities from Puerto Rico, which will help ameliorate a critical public health research gap (since prior studies have focused overwhelmingly on White, high socio-economic status samples, with only limited relevance to historically marginalized and at-risk communities). In addition, educational activities will engage high school through graduate students to create a pipeline of future data scientists and engineers that democratizes access to broader communities. To maximize impact, project outcomes will be disseminated in peer-reviewed articles, outreach programs, and open code/data repositories.The goal of this work is to establish a computer vision-based, artificial intelligence-guided infant motor function monitoring and assessment system to enable unobtrusive tracking of measures of motor impairment while the infant is in their natural environment. To this end, the research will learn and quantify visual representations of motor function in infants and develop novel data/label-efficient AI techniques, including biomechanically constrained synthetic data augmentation, semantic-aware domain adaptation, and human-AI co-labeling algorithms. The collaboration with the Puerto Rico Testsite for Exploring Contamination Threats (PROTECT) cohort will enable large-scale clinical validation of the extracted measures of early motor function in infants between the ages of 5-10 months and their relationship with the standardized risk screening tests performed at 18 and 24 months of age. A public outreach activity presenting live demonstrations of the developed AI tools will help raise awareness of the importance of AI-guided automatic motor function monitoring early in life.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.

该奖项的全部或部分资金来自《2021年美国救援计划法案》(公法117-2)。自闭症谱系障碍(ASD)是一种神经发育状况，在高达1.5%的人口中影响社会沟通和灵活行为。运动障碍被认为是自闭症的最初症状之一，可能先于社会或语言异常。但运动质量的差异也与其他疾病有关，如脑瘫和发育协调障碍，因此，尽管早期运动缺陷本身并不能诊断自闭症，但它们是风险指标。这项研究将直接帮助及早识别婴儿的运动障碍，从而使早期治疗能够带来更好的生活质量以及降低医疗保健和教育成本。更广泛的影响将来自波多黎各，这将有助于改善公共卫生研究的一个关键差距，这将有助于改善服务不足的低收入人口(因为以前的研究绝大多数集中在白人、社会经济地位较高的样本上，与历史上被边缘化和处于危险中的社区的相关性有限)。此外，教育活动将让高中到研究生建立一条未来数据科学家和工程师的渠道，使接触更广泛社区的途径民主化。为了最大限度地发挥影响，项目成果将在同行评议的文章、推广计划和开放代码/数据库中传播。这项工作的目标是建立一个基于计算机视觉的、人工智能指导的婴儿运动功能监测和评估系统，以便能够在婴儿处于自然环境中时对运动障碍的测量进行非显眼的跟踪。为此，该研究将学习和量化婴儿运动功能的视觉表示，并开发新的数据/标签高效人工智能技术，包括生物力学约束的合成数据增强、语义感知领域适应和人类-人工智能联合标记算法。与波多黎各测试网站合作探索污染威胁(PROTECT)队列，将能够大规模临床验证提取的5-10个月婴儿早期运动功能测量及其与18-24个月婴儿进行的标准化风险筛查测试的关系。一个展示开发的人工智能工具的公共推广活动将有助于提高人们对人工智能引导的早期自动运动功能监测的重要性的认识。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Appearance-Independent Pose-Based Posture Classification in Infants

婴儿中与外观无关的基于姿势的姿势分类

• 发表时间: 2022
• 期刊: 2022 26th International Conference on Pattern Recognition (ICPR
• 作者: Xiaofei Huang, Shuangjun Liu

InfAnFace: Bridging the Infant–Adult Domain Gap in Facial Landmark Estimation in the Wild

• DOI: 10.1109/icpr56361.2022.9956647
• 发表时间: 2021-10
• 期刊: 2022 26th International Conference on Pattern Recognition (ICPR)
• 作者: Michael Wan;S. Zhu;Prateek Gulati;L. Luan;X. Huang;R. Schwartz-Mette;M. Hayes;E. Zimmerman;S. Ostadabbas

Computer Vision to the Rescue: Infant Postural Symmetry Estimation from Incongruent Annotations

计算机视觉来救援：根据不一致的注释估计婴儿姿势对称性

• 发表时间: 2023
• 期刊: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision
• 作者: Xiaofei Huang, Michael Wan

Automatic Assessment of Infant Face and Upper-Body Symmetry as Early Signs of Torticollis

自动评估婴儿面部和上身对称性作为斜颈的早期症状

• 发表时间: 2023
• 期刊: IEEE conference series on Automatic Face and Gesture Recognition (FG
• 作者: Wan, Michael;Huang, Xiaofei;Tunik, Bethany;Ostadabbas, Sarah
• 通讯作者: Ostadabbas, Sarah