CHS: Large: Collaborative Research: Computational Science for Improving Assessment of Executive Function in Children

CHS：大：合作研究：改善儿童执行功能评估的计算科学

• 批准号: 1565328
• 负责人: Fillia Makedon
• 金额: $ 126.75万
• 依托单位: University of Texas at Arlington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-10-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1565328&HistoricalAwards=false
• 关键词: CHS; Large; Collaborative; Research; Computational

The identification of cognitive impairments in early childhood provides the best opportunity for successful remedial intervention, because brain plasticity diminishes with age. Attention deficit hyperactivity disorder (ADHD) is a psychiatric neurodevelopmental disorder that is very hard to diagnose or tell apart from other disorders. Symptoms include inattention, hyperactivity, or acting impulsively, all of which often result in poor performance in school and persist later in life. In this project, an interdisciplinary team of computer and neurocognitive scientists will develop and implement transformative computational approaches to evaluate the cognitive profiles of young children and to address these issues. The project will take advantage of both physical and computer based exercises already in place in 300 schools in the United States and involving thousands of children, many of whom have been diagnosed with ADHD or other learning disabilities. Project outcomes will have important implications for a child's success in school, self-image, and future employment and community functioning. The PIs will discover new knowledge about the role of physical exercise in cognitive training, including correlations between individual metrics and degree of improvement over time. They will identify important new metrics and correlations currently unknown to cognitive scientists, which will have broad impact on other application domains as well. And the PIs will develop an interdisciplinary course on computational cognitive science and one on user interfaces for neurocognitive experts.The research will involve four thrusts. The PIs will devise new human motion analysis and computer vision algorithms that can automatically assess embodied cognition during structured physical activities, and which will constitute a breakthrough in improving the accuracy and efficiency of cognitive assessments of young children. Intelligent mining techniques will be used to discover new knowledge about the role of physical exercise in cognitive training and to find correlations between individual metrics and degree of improvement over time. A methodology will be developed using advanced multimodal sensing to collect and process huge amounts of evidence based assessment data with intelligent mechanisms that learn about a child's executive function capabilities and help uncover possible causes of cognitive dysfunctions. And a closed loop cognitive assessment system will be designed and implemented to understand and monitor a child's progress over time and provide recommendations and decision support to cognitive experts so they can make better treatment decisions.

幼儿期认知障碍的识别为成功的补救干预提供了最佳机会，因为大脑可塑性随着年龄的增长而减少。 注意缺陷多动障碍（ADHD）是一种精神病神经发育障碍，很难诊断或分开其他疾病。 症状包括注意力不集中，多动症或行为，所有这些症状通常都导致学校表现不佳，并且在以后的生活中持续存在。 在这个项目中，计算机和神经认知科学家的跨学科团队将开发和实施变革性计算方法，以评估幼儿的认知概况并解决这些问题。 该项目将利用美国300所学校已经进行的身体和计算机练习，并涉及成千上万的儿童，其中许多人被诊断出患有多动症或其他学习障碍。 项目成果将对孩子在学校，自我形象以及未来的就业和社区功能方面的成功有重要影响。 PI将发现有关体育锻炼在认知训练中的作用的新知识，包括个人指标与随着时间的改进程度之间的相关性。 他们将确定认知科学家目前未知的重要新指标和相关性，这也将对其他应用领域产生广泛的影响。 PIS将开发有关计算认知科学的跨学科课程，以及关于神经认知专家的用户界面的一门课程。该研究将涉及四个推力。 PI将设计新的人类运动分析和计算机视觉算法，这些算法可以自动评估结构化体育活动期间体现的认知，这将构成提高幼儿认知评估的准确性和效率方面的突破。 智能采矿技术将用于发现有关体育锻炼在认知训练中的作用的新知识，并发现个人指标与随着时间的改进程度之间的相关性。 将使用先进的多模式传感来开发一种方法，以通过智能机制收集和处理大量基于证据的评估数据，以了解儿童的执行功能能力，并有助于发现认知功能障碍的可能原因。 并且将设计和实施一个封闭的循环认知评估系统，以了解和监视孩子的进步，并向认知专家提供建议和决策支持，以便他们可以做出更好的治疗决策。

项目成果

A Human Robot Interaction Framework for Robotic Motor Skill Learning

用于机器人运动技能学习的人机交互框架

• DOI: 10.1145/3197768.3197790
• 发表时间: 2018
• 期刊: Proceedings of the 11th PErvasive Technologies Related to Assistive Environments Conference on - PETRA '18
• 作者: Theofanidis, Michail;Cloud, Joe;Babu, Ashwin Ramesh;Brady, James;Makedon, Fillia
• 通讯作者: Makedon, Fillia

Towards predicting task performance from EEG signals

• DOI: 10.1109/bigdata.2017.8258478
• 发表时间: 2017-12
• 期刊: 2017 IEEE International Conference on Big Data (Big Data)
• 作者: Michalis Papakostas;K. Tsiakas;Theodoros Giannakopoulos;F. Makedon

Towards Deep Learning based Hand Keypoints Detection for Rapid Sequential Movements from RGB Images

基于深度学习的手部关键点检测，用于 RGB 图像中的快速连续运动

• DOI: 10.1145/3197768.3201538
• 发表时间: 2018
• 期刊: Proceedings of the 11th PErvasive Technologies Related to Assistive Environments Conference on - PETRA '18
• 作者: Gattupalli, Srujana;Babu, Ashwin Ramesh;Brady, James Robert;Makedon, Fillia;Athitsos, Vassilis
• 通讯作者: Athitsos, Vassilis

Task Engagement as Personalization Feedback for Socially-Assistive Robots and Cognitive Training

任务参与作为社交辅助机器人和认知训练的个性化反馈

• DOI: 10.3390/technologies6020049
• 发表时间: 2018
• 期刊: Technologies
• 影响因子: 3.6
• 作者: Tsiakas, Konstantinos;Abujelala, Maher;Makedon, Fillia
• 通讯作者: Makedon, Fillia

v-CAT: A Cyberlearning Framework for Personalized Cognitive Skill Assessment and Training

v-CAT：个性化认知技能评估和培训的网络学习框架

• DOI: 10.1145/3197768.3201545
• 发表时间: 2018
• 期刊: Proceedings of the 11th PErvasive Technologies Related to Assistive Environments Conference on - PETRA '18
• 作者: Papakostas, Michalis;Tsiakas, Konstantinos;Abujelala, Maher;Bell, Morris;Makedon, Fillia
• 通讯作者: Makedon, Fillia