Human body motions can be captured as a high-dimensional continuous signal using motion sensor technologies. The resulting data can be surprisingly rich in information, even when captured from persons with limited mobility. In this work, we explore the use of limited upper-body motions, captured via motion sensors, as inputs to control a 7 degree-of-freedom assistive robotic arm. It is possible that even dense sensor signals lack the salient information and independence necessary for reliable high-dimensional robot control. As the human learns over time in the context of this limitation, intelligence on the robot can be leveraged to better identify key learning challenges, provide useful feedback, and support individuals until the challenges are managed. In this short paper, we examine two uninjured participants' data from an ongoing study, to extract preliminary results and share insights. We observe opportunities for robot intelligence to step in, including the identification of inconsistencies in time spent across all control dimensions, asymmetries in individual control dimensions, and user progress in learning. Machine reasoning about these situations may facilitate novel interface learning in the future.
翻译:人体运动可以用运动传感器技术作为高维持续信号。 由此产生的数据在信息方面可能令人惊讶地丰富, 即使从行动能力有限的人那里获取。 在这项工作中,我们探索使用有限的上体运动,通过运动传感器捕获,作为控制7度自由辅助机器人臂的投入。甚至密度大的传感器也有可能缺乏可靠的高维机器人控制所必需的显著信息和独立性。随着人类在这一限制背景下的学习时间的不断演变,可以利用机器人的情报更好地识别关键的学习挑战,提供有用的反馈,并在应对挑战之前支持个人。在这份简短的论文中,我们研究了目前研究的两个无伤参与者的数据,提取初步结果并分享洞察力。我们观察机器人情报进入所有控制层面的机会,包括查明个人控制层面所用时间的不一致性,以及用户学习的进展。关于这些情况的机器推理可能会促进未来新的界面学习。