Collaborative Research: Teaching Human Motion Tasks at Population Scale

该项目将开发运动任务教学的技术和研究方法，手语教学是第一个应用。身体各部分的同时放置或快速运动是很难观察、解释和执行的。虽然触觉传感和增强现实系统已经被开发出来，以实现物理过程的人机交流，但重点主要放在执行上，而不是教与学。该项目最初的重点将放在手语教学上，但所发现的原理和技术将被推广到研究日益复杂的身体运动的学习，从简单的摆姿势任务到高速精细操作任务。提议的工作是变革性的，因为它将直接解决如何使用技术来理解正确或不正确的人类运动的科学问题，并提供建设性的指导，从而更好地理解人类运动学习。该项目将通过传统的科学出版物传播研究结果和资源。此外，模型、算法和设计的快速原型工具操作将在网上提供。研究结果还将通过与当地高中和博物馆的合作，以及通过参加美国科学与工程节等活动进行广泛传播。运动教学任务激发了三个基本挑战的研究。首先，闭环控制是网络物理系统的核心特征。在系统中有了人类参与者，如何才能围绕缓慢和低带宽的人类注意力关闭这个循环？引导人类的驱动必须易于沟通，并足以稳定人体套装系统。其次，由于所能传达的信息量有限，复杂的人体动作必须分解，各个部分必须单独教授。如何发现、教授和重新整合这些组成动作？第三，必须开发算法和系统来衡量学习者在教学过程中的准确性和记忆力，指导练习材料的重复和选择。该项目将设计和建造一个轻型传感和制导系统，允许人与计算机之间的运动交互通信。这项技术将使研究人员能够解决网络学习中关于如何更好地教授和学习人类运动任务的基本研究问题。研究问题包括如何测量和评估与任务相关的人体运动，如何选择感官输入作为指导，以及如何有选择地应用或移除训练辅助，直到学习者能够在没有帮助的情况下完成运动任务。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The project will develop technology and study methods for teaching motion tasks, with the teaching of sign language as a first application. Simultaneous placement or quick movement of parts of the body is hard to observe, explain, and execute. While tactile-sensing and augmented-reality systems have been developed to enable machine-human communication of physical processes, the focus has largely been on execution, rather than on teaching and learning. The initial focus of the project will be on teaching sign language, but principles and techniques discovered will be generalized to research the learning of increasingly complex physical motions, ranging from simple posing tasks to high-speed fine manipulation tasks. The proposed work is transformative in that it will directly address the scientific question of how to use technology to understand correct or incorrect human motion and provide constructive guidance, leading to a better understanding of human motion learning. The project will disseminate findings and resources through traditional scientific publications. In addition, models, algorithms, and designs for rapidly-prototyped tools for manipulation will be made available on the online. Results will also be communicated broadly through collaborations with local high schools and museums, and through participation in events such as the USA Science and Engineering Festival.The task of teaching motion motivates the research of three fundamental challenges. First, closed-loop control is a core feature of cyber-physical systems. With a human participant in the system, how can the loop be closed around slow and low-bandwidth human attention? Actuation that guides the human must be easily communicated and sufficient to stabilize the human-suit system. Second, due to limitations in how much information may be communicated, complex human motions must be broken down, and components taught in isolation. How can these component motions be discovered, taught, and re-integrated? Third, algorithms and systems must be developed to measure the accuracy and retention of the learner during the teaching process, guiding repetition and selection of practice material. The project will design and build a lightweight sensing and guidance system that allows interactive communication about motion between human and computer. This technology will allow the investigators to address fundamental research questions in cyber-learning about how to better teach and learn human motion tasks. Research questions include how to measure and evaluate human motion with respect to the task, how to select sensory input to use as guidance, and how to selectively apply or remove training aids, until the learner can complete the motion task with no assistance.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.