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

基本信息

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

项目摘要

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.

儿童早期认知障碍的识别为成功的治疗干预提供了最佳机会，因为大脑的可塑性随着年龄的增长而减弱。注意力缺陷多动障碍（ADHD）是一种精神神经发育障碍，很难诊断或与其他疾病区分开。症状包括注意力不集中、多动或行为冲动，所有这些通常会导致在学校的表现不佳，并在以后的生活中持续存在。在该项目中，由计算机和神经认知科学家组成的跨学科团队将开发和实施变革性计算方法来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 所学校已经开展的物理和计算机练习，涉及数千名儿童，其中许多儿童被诊断患有多动症或其他学习障碍。项目成果将对儿童在学校的成功、自我形象以及未来的就业和社区运作产生重要影响。 PI 将发现有关体育锻炼在认知训练中的作用的新知识，包括个人指标与随时间推移的改善程度之间的相关性。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影响。 PI 将为神经认知专家开发一门关于计算认知科学的跨学科课程和一门关于用户界面的课程。该研究将涉及四个重点。 PI将设计新的人体运动分析和计算机视觉算法，可以自动评估结构化身体活动期间的具身认知，这将在提高幼儿认知评估的准确性和效率方面取得突破。智能挖掘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推移的改善程度之间的相关性。将开发一种方法，使用先进的多模式传感来收集和处理大量基于证据的评估数据，并通过智能机制了解儿童的执行功能能力并帮助发现认知功能障碍的可能原因。将设计和实施闭环认知评估系统，以了解和监测儿童随时间的进展，并向认知专家提供建议和决策支持，以便他们做出更好的治疗决策。

项目成果

期刊论文数量（9）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

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
影响因子：
0
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)
影响因子：
0
作者：
Michalis Papakostas;K. Tsiakas;Theodoros Giannakopoulos;F. Makedon
通讯作者：
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
影响因子：
0
作者：
Gattupalli, Srujana;Babu, Ashwin Ramesh;Brady, James Robert;Makedon, Fillia;Athitsos, Vassilis
通讯作者：
Athitsos, Vassilis

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
影响因子：
0
作者：
Papakostas, Michalis;Tsiakas, Konstantinos;Abujelala, Maher;Bell, Morris;Makedon, Fillia
通讯作者：
Makedon, Fillia

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