Sensory Motor Performance Program, Rehabilitation Institute of Chicago Chicago, IL, USA ; Department of Physical Medicine and Rehabilitation, Northwestern University Chicago, IL, USA ; Melbourne School of Psychological Sciences, University of Melbourne Parkville, VIC, Australia.
Department of Biomedical Engineering, Northwestern University Evanston, IL, USA.
Front Neurosci. 2014 May 23;8:123. doi: 10.3389/fnins.2014.00123. eCollection 2014.
Cervical spinal cord injury (SCI) paralyzes muscles of the hand and arm, making it difficult to perform activities of daily living. Restoring the ability to reach can dramatically improve quality of life for people with cervical SCI. Any reaching system requires a user interface to decode parameters of an intended reach, such as trajectory and target. A challenge in developing such decoders is that often few physiological signals related to the intended reach remain under voluntary control, especially in patients with high cervical injuries. Furthermore, the decoding problem changes when the user is controlling the motion of their limb, as opposed to an external device. The purpose of this study was to investigate the benefits of combining disparate signal sources to control reach in people with a range of impairments, and to consider the effect of two feedback approaches. Subjects with cervical SCI performed robot-assisted reaching, controlling trajectories with either shoulder electromyograms (EMGs) or EMGs combined with gaze. We then evaluated how reaching performance was influenced by task-related sensory feedback, testing the EMG-only decoder in two conditions. The first involved moving the arm with the robot, providing congruent sensory feedback through their remaining sense of proprioception. In the second, the subjects moved the robot without the arm attached, as in applications that control external devices. We found that the multimodal-decoding algorithm worked well for all subjects, enabling them to perform straight, accurate reaches. The inclusion of gaze information, used to estimate target location, was especially important for the most impaired subjects. In the absence of gaze information, congruent sensory feedback improved performance. These results highlight the importance of proprioceptive feedback, and suggest that multi-modal decoders are likely to be most beneficial for highly impaired subjects and in tasks where such feedback is unavailable.
颈椎脊髓损伤 (SCI) 会导致手部和手臂肌肉瘫痪，使日常生活活动变得困难。恢复伸手能力可以显著提高颈 SCI 患者的生活质量。任何伸手系统都需要一个用户界面来解码预期伸手的参数，例如轨迹和目标。开发这种解码器的一个挑战是，通常很少有与预期伸手相关的生理信号仍然可以受到自愿控制，尤其是在高颈损伤的患者中。此外，当用户控制肢体运动时，解码问题会发生变化，而不是外部设备。本研究的目的是调查结合不同信号源控制不同损伤程度患者伸手的益处，并考虑两种反馈方法的影响。颈 SCI 患者进行了机器人辅助伸手，使用肩部肌电图 (EMG) 或 EMG 与凝视相结合来控制轨迹。然后，我们评估了与任务相关的感官反馈如何影响伸手表现，在两种情况下测试了仅使用 EMG 的解码器。第一种情况是在机器人的帮助下移动手臂，通过剩余的本体感觉提供一致的感觉反馈。在第二种情况下，手臂未连接的情况下，受试者移动机器人，就像控制外部设备的应用程序一样。我们发现，多模态解码器对所有受试者都有效，使他们能够进行笔直、准确的伸手。使用凝视信息来估计目标位置的信息尤其重要，因为它对最受损的受试者很重要。在没有凝视信息的情况下，一致的感觉反馈会提高性能。这些结果强调了本体感觉反馈的重要性，并表明多模态解码器可能对高度受损的患者和在无法提供此类反馈的任务中最有益。
