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

项目总结/摘要 管理日益增长的多样性对美国公共部门来说是一个持续的挑战和机遇。 公众对神经多样性运动的关注，认识到神经差异是 一个人的身份，多年来一直在增长。然而，目前还不确定如何 促进和支持这种新的多样性，特别是在工作场所。 患有自闭症谱系障碍（ASD）的成年人在儿童中的代表性严重不足。 职场新兴的工作工具和技术（例如，协作机器人，虚拟现实[VR]） 嵌入了人工智能（AI）/机器学习（ML）， 目前和未来工作所需的基本技能。这些技能包括解决问题， 协作、社交智能和沟通。自闭症患者通常表现出 在这些和相关技能的差异，并继续遇到障碍， 维持就业。我们的长期目标是促进有效合作， 自闭症成年人和他们在工作场所的同事之间的沟通。本课题 将（1）利用ML方法来识别和分类生理，认知，行为， 情绪，和参与状态的神经分歧的个人在一个合作， 人的任务和（2）学习和预测合作行为模式的动态过程中 在远程工作环境中解决复杂问题。 在目标1中，为了理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，包括使用LEGO®积木进行模拟组装任务。多式 数据（例如，生理同步，面部表情）从三个不同的每个成员 将收集二元组（自闭症-自闭症、自闭症-非自闭症和非自闭症-非自闭症）。 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）将使用 隐马尔可夫模型（HMM）。在目标2中，虚拟乐高®组装任务将由以下人员执行： 二元团队在远程工作环境中检查基于ML的方法（在目标1中开发）。 完成这一发展项目将为今后努力扩大 相关的研究能力和创新研究，如工作场所的进步 设计指导方针和技术，以促进和支持有效的神经多样性工作场所。

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.