Development of machine learning methods to support collaboration in a neurodiverse team at work

开发机器学习方法以支持神经多元化团队在工作中的协作

• 批准号: 10432554
• 负责人: Sun Wook Kim
• 金额: $ 7.11万
• 依托单位: VIRGINIA POLYTECHNIC INST AND ST UNIV
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-05-15 至 2024-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10432554
• 关键词: Adopted; Affect; Artificial Intelligence; Attention; Behavior; Behavioral; Characteristics; Classification; Cognitive; Collaborations; Communication; Complex; Data; Development; Dictionary; Dimensions; Emotional; Employment; Environment; Exhibits; Face; Facial Expression; Foundations; Future; Goals; Guidelines; Independent Living; Individual; Label; Learning; Machine Learning; Measures; Methods; Modeling; Movement; Neurologic; Occupations; Pattern; Persons; Physiological; Prevalence; Problem Solving; Process; Property; Public Sector; Research; Robot; Self-Injurious Behavior; Social intelligence; Support System; System; Technology; Unemployment; Validation; Work; Workplace; adult with autism spectrum disorder; autism spectrum disorder; autistic; autistic children; base; cognitive function; design; experience; heart rate monitor; improved; individuals with autism spectrum disorder; innovation; long short term memory network; machine learning algorithm; machine learning method; markov model; member; multimodal data; sensor; skills; social; social communication; tool; virtual; virtual reality

Project Summary/Abstract Managing growing diversity is an ongoing challenge and opportunity for the U.S. public sector. Public attention to the neurodiversity movement, recognizing neurological differences as the identity of an individual, has been growing over the years. Yet, it is still uncertain how to promote and support this new dimension of diversity especially in the workplace. Adults with autism spectrum disorder (ASD) are substantially underrepresented in the workplace. Emerging work tools and technologies (e.g., collaborative robots, virtual reality [VR]) embedded with artificial intelligence (AI)/machine learning (ML) are greatly affecting fundamental skills required for current and future jobs. Such skills include problem solving, collaboration, social intelligence, and communication. Autistic individuals generally show differences in these and related skills, and have continued to experience barriers in finding and maintaining employment. Our long-term goal is to promote effective collaboration and communication between autistic adults and their coworkers in the workplace. In this project, we will (1) leverage an ML approach to recognize and classify physiological, cognitive, behavioral, emotional, and engagement states of neurodivergent individuals during a collaborative in- person task and (2) learn and predict the dynamics of collaborative behavioral patterns during complex problem solving exhibited in a remote work setting. In Aim 1, to understand collaboration processes and strategies of a neurodiverse team, we will conduct a lab study that involves a simulated assembly task using LEGO® blocks. Multimodal data (e.g., physiological synchrony, facial expression) from each member of three different dyadic teams (autistic-autistic, autistic-nonautistic, and nonautistic-nonautistic) will be collected. Detailed labels (for ML algorithms) will be developed to reflect underlying properties of collaborative processes, and strategies (e.g., sequences of processes) will be modeled with a Hidden Markov model (HMM). In Aim 2, a virtual LEGO® assembly task will be performed by dyadic teams to examine the ML-based approach (developed in Aim 1) in a remote work setting. Completing this developmental project will establish a foundation for future efforts to extend relevant research capabilities and innovative research, such as the advancement of workplace design guidelines and technology, to promote and support an effective neurodiverse workplace.

项目摘要/摘要 管理不断增长的多样性是美国公共部门的持续挑战和机会。 公众对神经多样性运动的关注，认识到神经系统差异 多年来，个人的身份一直在增长。但是，仍然不确定如何 促进和支持这一多样性的新维度，尤其是在工作场所。 自闭症谱系障碍（ASD）的成年人在大幅代表性中的代表性不足 工作场所。新兴的工作工具和技术（例如协作机器人，虚拟现实[VR]） 嵌入人工智能（AI）/机器学习（ML）的影响很大 当前和将来的工作所需的基本技能。这样的技能包括解决问题， 协作，社会情报和沟通。自闭症患者通常显示 这些和相关技能的差异，并继续在查找和 维持就业。我们的长期目标是促进有效的合作和 令人印象深刻的成年人与他们的同事之间的沟通。在这个项目中，我们 （1）利用ML识别和分类生理，认知，行为， 在协作期间，神经异常个体的情感和参与状态 人任务和（2）在期间学习和预测协作行为模式的动态 在远程工作环境中暴露的复杂问题解决。 在AIM 1中，要了解神经多样性团队的协作过程和策略，我们将 进行实验室研究，该研究涉及使用LEGO®块进行模拟的组装任务。多模式 来自三个不同的每个成员的数据（例如，物理同步，面部表达） 将收集二元组（自闭症自闭症，自闭症和非自然主义者）。 将开发详细的标签（用于ML算法），以反映 协作过程和策略（例如，过程序列）将以一个 隐藏的马尔可夫模型（嗯）。在AIM 2中，将执行虚拟LEGO®装配任务 在远程工作环境中检查基于ML的方法（在AIM 1中开发）的二元团队。 完成此开发项目将为将来的努力奠定基础 相关的研究能力和创新研究，例如工作场所的发展 设计指南和技术，以促进和支持有效的神经多样性工作场所。