Collaborative Research: CCRI: New: An Open Data Infrastructure for Bodily Expressed Emotion Understanding

合作研究:CCRI:新:用于理解身体表达情绪的开放数据基础设施

基本信息

  • 批准号:
    2234195
  • 负责人:
  • 金额:
    $ 183.23万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2023
  • 资助国家:
    美国
  • 起止时间:
    2023-04-15 至 2026-03-31
  • 项目状态:
    未结题

项目摘要

The project goal is to unlock the wealth of information about human expression that is already found in videos on the internet. The multidisciplinary project team will collect videos of human movement available online and use experts in movement analysis and non-experts to pinpoint at characteristics of the human movement that can be used to drive algorithms that will attempt to classify the emotion expressed by the human mover. These characteristics will form labels on the data that include context, demographics, technical concepts from movement analysis, and emotion. This work will take an unprecedented, multidisciplinary approach in creating a data infrastructure for computational modeling of bodily expression of emotion. To ensure the infrastructure's compatibility with human-robot interaction research, the team will conduct a public-facing feasibility study. The team will also employ advisory boards and continue to engage with active researchers in multiple sub-disciplines of the computer and information science and engineering research community in the designing, creation, testing, and dissemination of the data infrastructure, and organizing annual user community workshops and benchmarking challenges. The data infrastructure is expected to promote technological innovations and breakthroughs in data-driven modeling of human bodily expression of emotion and affect, a highly complex problem with applications in healthcare, e.g., caregiving robots and diagnostic tools for mental health, manufacturing, e.g., socially-aware autonomous forklifts and safety monitoring systems, security, e.g., monitoring, and consumer electronics, e.g., improved interactions with a home robot.Bodily movement expresses important information, including conveying emotion, which is crucial for future human-machine interactions. As in other areas of artificial intelligence (AI), such as image recognition, a large-scale data-driven approach holds promise for revealing new insights into the complex, subtle, and contextual nature of human bodily expression. However, research on computational recognition of bodily expression, an area of affective computing, AI, and human-robot interaction, is struggling to mature as researchers must replicate many of the same work-intensive steps, creating divergent efforts and expense. This NSF project aims to create a large-scale, high-quality, multifaceted, annotated, open, and extensible data infrastructure for computational understanding of human bodily expressions in a variety of settings. It will leverage the team's expertise in AI, computer vision, affective computing, expressive robotics, emotion recognition, psychology, movement analysis, statistics and data mining, data ethics, and the arts to create (1) a data-sharing infrastructure tailored to the needs of research into subjective experience, emotion, and bodily movement, (2) a crowdsourced annotated video dataset, and (3) a collection of tools and software for rigorous reliability validation, reproducibility and transparency assessment, and content-based search and retrieval. The data infrastructure is expected to serve applications in fields such as robotics, psychology, performing arts, animation, and entertainment. The project also develops human expertise in this emerging field by supporting graduate and undergraduate students, including students from underrepresented groups, providing experience in conducting infrastructure development, integrating knowledge from multiple disciplines. These students will interact regularly with the team’s international partners. Public events that create broad public engagement in the work will focus on numerous applications to human-robot interaction. The infrastructure will stimulate focused research projects and agendas in affective computing, AI, including artificial emotional intelligence and human-AI interaction, computer vision, social/assistive robotics, virtual agents, psychiatric telemedicine, human-centered design, machine/deep learning, ethics in computing, and related communities.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.
该项目的目标是解锁互联网视频中已经存在的有关人类表达的丰富信息,多学科项目团队将收集在线提供的人类运动视频,并利用运动分析专家和非专家来查明人类运动的特征。人类运动可用于驱动算法,这些算法将尝试对人类运动者表达的情绪进行分类,这些特征将在数据上形成标签,其中包括上下文、人口统计、运动分析的技术概念和情绪。创建数据基础设施时采用前所未有的多学科方法为了确保基础设施与人机交互研究的兼容性,该团队还将聘请顾问委员会并继续与多个子领域的活跃研究人员合作。计算机和信息科学与工程研究界在数据基础设施的设计、创建、测试和传播方面的学科,并组织年度用户社区研讨会和基准测试挑战赛,数据基础设施有望促进数据基础设施的技术创新和突破。人体表达的驱动建模情感和情感,是医疗保健应用中一个高度复杂的问题,例如用于心理健康的护理机器人和诊断工具、制造(例如具有社会意识的自动叉车和安全监控系统)、安全(例如监控)和消费电子产品(例如)改善与家庭机器人的交互。身体运动表达重要信息,包括传达情感,这对于未来的人机交互至关重要,就像图像识别等人工智能 (AI) 领域一样。大规模数据驱动的方法有望揭示人类身体表达复杂、微妙和情境性质的新见解,然而,对身体表达的计算识别(情感计算、人工智能和人类的一个领域)的研究。机器人交互正在努力成熟,因为研究人员必须多次重复相同的工作密集型步骤,从而产生不同的工作和费用。该 NSF 项目旨在创建大规模、高质量、多方面、带注释、开放和可扩展的数据。用于计算理解人体表达的基础设施它将利用团队在人工智能、计算机视觉、情感计算、表达机器人、情感识别、心理学、运动分析、统计和数据挖掘、数据伦理和艺术方面的专业知识来创建 (1) 数据。 - 根据主观体验、情感和身体运动研究需求量身定制的共享基础设施,(2) 众包注释视频数据集,以及 (3) 用于严格可靠性验证、可重复性和透明度评估以及内容的工具和软件集合基于搜索和该数据基础设施预计将服务于机器人、心理学、表演艺术、动画和娱乐等领域的应用,该项目还通过支持研究生和本科生(包括来自代表性不足群体的学生)来发展人类专业知识。这些学生将定期与该团队的国际合作伙伴进行基础设施开发方面的经验交流,以促进公众广泛参与该工作,该基础设施将刺激有针对性的研究。项目和议程情感计算、人工智能,包括人工情感智能和人机交互、计算机视觉、社交/辅助机器人、虚拟代理、精神科远程医疗、以人为本的设计、机器/深度学习、计算伦理以及相关社区。该奖项反映了通过使用基金会的智力价值和更广泛的影响审查标准进行评估,NSF 的法定使命被认为值得支持。

项目成果

期刊论文数量(4)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Tutorial on Movement Notation: An Interdisciplinary Methodology for HRI to Reveal the Bodily Expression of Human Counterparts via Collecting Annotations from Dancers in a Shared Data Repository
动作注释教程:HRI 的跨学科方法,通过在共享数据存储库中收集舞者的注释来揭示人类对应者的身体表达
Bodily expressed emotion understanding through integrating Laban movement analysis
通过整合拉班运动分析来理解身体表达的情绪
  • DOI:
    10.1016/j.patter.2023.100816
  • 发表时间:
    2023-04-05
  • 期刊:
  • 影响因子:
    6.5
  • 作者:
    Chenyan Wu;Dolzodmaa Davaasuren;T. Shafir;Rachelle Tsachor;James Ze Wang
  • 通讯作者:
    James Ze Wang
Unlocking the Emotional World of Visual Media: An Overview of the Science, Research, and Impact of Understanding Emotion
解锁视觉媒体的情感世界:理解情感的科学、研究和影响概述
  • DOI:
    10.1109/jproc.2023.3273517
  • 发表时间:
    2023-10
  • 期刊:
  • 影响因子:
    20.6
  • 作者:
    Wang, James Z.;Zhao, Sicheng;Wu, Chenyan;Adams, Reginald B.;Newman, Michelle G.;Shafir, Tal;Tsachor, Rachelle
  • 通讯作者:
    Tsachor, Rachelle
A Machine Learning Paradigm for Studying Pictorial Realism: How Accurate are Constable's Clouds?
研究图像现实主义的机器学习范式:康斯特布尔的云有多准确?
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James Wang其他文献

The influence of electrokinetic bioremediation on subsurface microbial communities at a perchloroethylene contaminated site
动电生物修复对全氯乙烯污染场地地下微生物群落的影响
  • DOI:
    10.1007/s00253-021-11458-w
  • 发表时间:
    2021-08-01
  • 期刊:
  • 影响因子:
    5
  • 作者:
    M. Meinel;James Wang;E. Cox;P. Dennis;César I. Torres;R. Krajmalnik
  • 通讯作者:
    R. Krajmalnik
Identification of endothelial cell genes by combined database mining and microarray analysis.
通过数据库挖掘和微阵列分析相结合鉴定内皮细胞基因。
  • DOI:
    10.1152/physiolgenomics.00186.2002
  • 发表时间:
    2003-05-13
  • 期刊:
  • 影响因子:
    4.6
  • 作者:
    Michael Y. Ho;Eugene Yang;G. Matcuk;D. Deng;N. Sampas;A. Tsalenko;R. Tabibiazar;Ying Zhang;Mary M Chen;S. Talbi;Y. Ho;James Wang;P. Tsao;A. Ben;Z. Yakhini;L. Bruhn;T. Quertermous
  • 通讯作者:
    T. Quertermous
Influence of chelating agents on the microstructure and antibacterial property of cobalt ferrite nanopowders
螯合剂对钴铁氧体纳米粉体微观结构及抗菌性能的影响
  • DOI:
  • 发表时间:
    2013
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Noppakun Sanpo;James Wang;C. Berndt
  • 通讯作者:
    C. Berndt
Synthesis of 5,6-dihydro-11H-benzo[5,6]-cyclohepta[1,2-b]pyridin-11-ylidene)-1-piperidine-N-cyanoguanidine derivatives as inhibitors of ras farnesyl protein transferase.
合成 5,6-二氢-11H-苯并[5,6]-环庚[1,2-b]吡啶-11-亚基)-1-哌啶-N-氰基胍衍生物作为 ras 法呢基蛋白转移酶抑制剂。
  • DOI:
    10.1016/s0960-894x(01)00826-5
  • 发表时间:
    2002-02-25
  • 期刊:
  • 影响因子:
    2.7
  • 作者:
    A. Cooper;C. Strickland;James Wang;J. Desai;P. Kirschmeier;R. Patton;W. Bishop;P. Weber;V. Girijavallabhan
  • 通讯作者:
    V. Girijavallabhan
Baseline integrity property measurement of legacy oil and gas wells for carbon storage projects
碳封存项目遗留油气井的基线完整性测量
  • DOI:
  • 发表时间:
    2017
  • 期刊:
  • 影响因子:
    0
  • 作者:
    A. Duguid;R. Butsch;J. Carey;M. Celia;N. Chugunov;S. Gasda;T. Ramakrishnan;V. Stamp;James Wang
  • 通讯作者:
    James Wang

James Wang的其他文献

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{{ truncateString('James Wang', 18)}}的其他基金

SBIR Phase I: Engineering a novel 3D metal printed orthodontic system for lingual attachment-enabled clear aligner therapy
SBIR 第一阶段:设计新型 3D 金属打印正畸系统,用于支持舌侧附着的透明矫正器治疗
  • 批准号:
    1938533
  • 财政年份:
    2020
  • 资助金额:
    $ 183.23万
  • 项目类别:
    Standard Grant
CCRI: Planning: Planning to Develop a Body Language Dataset for the Artificial Intelligence Research Community
CCRI:规划:规划为人工智能研究界开发肢体语言数据集
  • 批准号:
    1921783
  • 财政年份:
    2019
  • 资助金额:
    $ 183.23万
  • 项目类别:
    Standard Grant
SoCS: Studying the Computability of Emotions by Harnessing Massive Online Social Data
SoCS:利用海量在线社交数据研究情绪的可计算性
  • 批准号:
    1110970
  • 财政年份:
    2011
  • 资助金额:
    $ 183.23万
  • 项目类别:
    Continuing Grant
CDI-Type I: International Collaboration to Study Oceanic Currents Phenomena and Climate Changes Through Cross-Mining and Retrieving Multispectral Satellite Image and Sensor Network
CDI-I型:通过交叉挖掘和检索多光谱卫星图像和传感器网络研究洋流现象和气候变化的国际合作
  • 批准号:
    1027854
  • 财政年份:
    2010
  • 资助金额:
    $ 183.23万
  • 项目类别:
    Standard Grant
EAGER: Analysis and Intelligent Search for Cypriot Works of Art and Secretariat Corpus
EAGER:塞浦路斯艺术品和秘书处语料库的分析和智能搜索
  • 批准号:
    0949891
  • 财政年份:
    2009
  • 资助金额:
    $ 183.23万
  • 项目类别:
    Standard Grant
ITR: Advancing Digital Imagery Technologies for Asian Art and Cultural Heritages
ITR:推进亚洲艺术和文化遗产的数字图像技术
  • 批准号:
    0219272
  • 财政年份:
    2002
  • 资助金额:
    $ 183.23万
  • 项目类别:
    Continuing Grant
Travel Grant to Participate in the International Scientific Conference on Digital Libraries in Russia
参加俄罗斯数字图书馆国际科学会议的旅费资助
  • 批准号:
    0112641
  • 财政年份:
    2001
  • 资助金额:
    $ 183.23万
  • 项目类别:
    Standard Grant
Helical and Superhelical Structure of DNA
DNA 的螺旋和超螺旋结构
  • 批准号:
    8807067
  • 财政年份:
    1988
  • 资助金额:
    $ 183.23万
  • 项目类别:
    Continuing Grant
Helical and Superhelical Structure of DNA
DNA 的螺旋和超螺旋结构
  • 批准号:
    8508151
  • 财政年份:
    1985
  • 资助金额:
    $ 183.23万
  • 项目类别:
    Continuing Grant
Helical and Superhelical Structure of DNA
DNA 的螺旋和超螺旋结构
  • 批准号:
    8116543
  • 财政年份:
    1982
  • 资助金额:
    $ 183.23万
  • 项目类别:
    Continuing Grant

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相似海外基金

Collaborative Research: CCRI: New: A Research News Recommender Infrastructure with Live Users for Algorithm and Interface Experimentation
合作研究:CCRI:新:研究新闻推荐基础设施与实时用户进行算法和界面实验
  • 批准号:
    2232552
  • 财政年份:
    2023
  • 资助金额:
    $ 183.23万
  • 项目类别:
    Standard Grant
Collaborative Research: CCRI: Planning-C: Enabling Computer Architecture Simulation as a Service
合作研究:CCRI:Planning-C:实现计算机架构仿真即服务
  • 批准号:
    2234401
  • 财政年份:
    2023
  • 资助金额:
    $ 183.23万
  • 项目类别:
    Standard Grant
Collaborative Research: CCRI: Planning-C: A Community for Configurability Open Research and Development (ACCORD)
合作研究:CCRI:Planning-C:可配置性开放研究与开发社区 (ACCORD)
  • 批准号:
    2234909
  • 财政年份:
    2023
  • 资助金额:
    $ 183.23万
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Collaborative Research: Research Infrastructure: CCRI:New: Data-Driven Cybersecurity Research Infrastructure for Smart Manufacturing
合作研究:研究基础设施:CCRI:新:数据驱动的智能制造网络安全研究基础设施
  • 批准号:
    2234973
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    2023
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Collaborative Research: CCRI: Grand: Quori 2.0: Uniting, Broadening, and Sustaining a Research Community Around a Modular Social Robot Platform
协作研究:CCRI:盛大:Quori 2.0:围绕模块化社交机器人平台联合、扩大和维持研究社区
  • 批准号:
    2235042
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    2023
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    $ 183.23万
  • 项目类别:
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