Abstract：
Body-machine interfaces (BoMIs)-systems that control assistive devices (e.g., a robotic manipulator) with a person\'s movements-offer a robust and non-invasive alternative to brain-machine interfaces for individuals with neurological injuries. However, commercially-available assistive devices offer more degrees of freedom (DOFs) than can be efficiently controlled with a user\'s residual motor function. Therefore, BoMIs often rely on nonintuitive mappings between body and device movements. Learning these mappings requires considerable practice time in a lab/clinic, which can be challenging. Virtual environments can potentially address this challenge, but there are limited options for high-DOF assistive devices, and it is unclear if learning with a virtual device is similar to learning with its physical counterpart. We developed a novel virtual robotic platform that replicated a commercially-available 6-DOF robotic manipulator. Participants controlled the physical and virtual robots using four wireless inertial measurement units (IMUs) fixed to the upper torso. Forty-three neurologically unimpaired adults practiced a target-matching task using either the physical (sample size n = 25) or virtual device (sample size n = 18) involving pre-, mid-, and post-tests separated by four training blocks. We found that both groups made similar improvements from pre-test in movement time at mid-test (Δvirtual: 9.9 ± 9.5 s; Δphysical: 11.1 ± 9.9 s) and post-test (Δvirtual: 11.1 ± 9.1 s; Δphysical: 11.8 ± 10.5 s) and in path length at mid-test (Δvirtual: 6.1 ± 6.3 m/m; Δphysical: 3.3 ± 3.5 m/m) and post-test (Δvirtual: 6.6 ± 6.2 m/m; Δphysical: 3.5 ± 4.0 m/m). Our results indicate the feasibility of using virtual environments for learning to control assistive devices. Future work should determine how these findings generalize to clinical populations.
摘要：
车身-机器接口(BoMI)-控制辅助设备的系统(例如，机器人操纵器）与人的运动-为神经系统受伤的人提供了一种强大且非侵入性的替代脑机接口的方法。然而,商用辅助设备提供了更多的自由度（DOF），可以有效地控制用户的剩余运动功能。因此,BoMI通常依赖于身体和设备运动之间的非直观映射。学习这些映射需要在实验室/诊所中花费大量的实践时间，这可能是具有挑战性的。虚拟环境可以潜在地解决这一挑战，但是高自由度辅助设备的选择有限，目前还不清楚使用虚拟设备学习是否与物理设备学习相似。我们开发了一种新颖的虚拟机器人平台，该平台复制了市售的6自由度机器人操纵器。参与者使用固定在上半身的四个无线惯性测量单元（IMU）控制物理和虚拟机器人。43个神经未受损的成年人使用物理（样本大小n=25）或虚拟设备（样本大小n=18）进行目标匹配任务，mid-,和由四个训练块分隔的后期测试。我们发现，两组在中期测试（Δ虚拟：9.9±9.5s；Δ物理：11.1±9.9s）和后期测试（Δ虚拟：11.1±9.1s；Δ物理：11.8±10.5s）的运动时间方面以及中期测试（Δ虚拟：6.1±6.3m/m；Δ物理：3.3±3.5m/m）和后期测试（Δ4.0：6.6±3.5m/m；我们的结果表明使用虚拟环境学习控制辅助设备的可行性。未来的工作应该确定这些发现如何推广到临床人群。
