Lee Jongmin M, Gebrekristos Temesgen, DE Santis Dalia, Nejati-Javaremi Mahdieh, Gopinath Deepak, Parikh Biraj, Mussa-Ivaldi Ferdinando A, Argall Brenna D
Northwestern University, USA and Shirley Ryan AbilityLab, USA.
Shirley Ryan AbilityLab, USA.
ACM Trans Hum Robot Interact. 2024 Sep;13(3). doi: 10.1145/3630264. Epub 2024 Aug 26.
When individuals are paralyzed from injury or damage to the brain, upper body movement and function can be compromised. While the use of body motions to interface with machines has shown to be an effective noninvasive strategy to provide movement assistance and to promote physical rehabilitation, learning to use such interfaces to control complex machines is not well understood. In a five session study, we demonstrate that a subset of an uninjured population is able to learn and improve their ability to use a high-dimensional Body-Machine Interface (BoMI), to control a robotic arm. We use a sensor net of four inertial measurement units, placed bilaterally on the upper body, and a BoMI with the capacity to directly control a robot in six dimensions. We consider whether the way in which the robot control space is mapped from human inputs has any impact on learning. Our results suggest that the space of robot control does play a role in the evolution of human learning: specifically, though robot control in joint space appears to be more intuitive initially, control in task space is found to have a greater capacity for longer-term improvement and learning. Our results further suggest that there is an inverse relationship between control dimension couplings and task performance.
当个体因脑部受伤或受损而瘫痪时，上半身的运动和功能可能会受到影响。虽然利用身体动作与机器交互已被证明是一种有效的非侵入性策略，可提供运动辅助并促进身体康复，但对于如何学习使用此类接口来控制复杂机器，人们还了解得不够深入。在一项为期五节的研究中，我们证明了一部分未受伤的人群能够学习并提高他们使用高维身体-机器接口（BoMI）来控制机械臂的能力。我们使用了一个由四个惯性测量单元组成的传感器网络，双侧放置在上半身，以及一个能够直接在六个维度上控制机器人的BoMI。我们考虑了机器人控制空间从人类输入映射的方式是否对学习有任何影响。我们的结果表明，机器人控制空间在人类学习的发展中确实起到了作用：具体而言，虽然关节空间中的机器人控制最初似乎更直观，但任务空间中的控制被发现具有更大的长期改进和学习能力。我们的结果进一步表明，控制维度耦合与任务性能之间存在反比关系。
