Department of Biomedical Engineering, University of Southern California, Los Angeles, California, United States of America.
Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy.
PLoS One. 2021 Apr 14;16(4):e0250001. doi: 10.1371/journal.pone.0250001. eCollection 2021.
The design of myocontrolled devices faces particular challenges in children with dyskinetic cerebral palsy because the electromyographic signal for control contains both voluntary and involuntary components. We hypothesized that voluntary and involuntary components of movements would be uncorrelated and thus detectable as different synergistic patterns of muscle activity, and that removal of the involuntary components would improve online EMG-based control. Therefore, we performed a synergy-based decomposition of EMG-guided movements, and evaluated which components were most controllable using a Fitts' Law task. Similarly, we also tested which muscles were most controllable. We then tested whether removing the uncontrollable components or muscles improved overall function in terms of movement time, success rate, and throughput. We found that removal of less controllable components or muscles did not improve EMG control performance, and in many cases worsened performance. These results suggest that abnormal movement in dyskinetic CP is consistent with a pervasive distortion of voluntary movement rather than a superposition of separable voluntary and involuntary components of movement.
肌控设备的设计在患有运动障碍性脑瘫的儿童中面临特殊挑战，因为用于控制的肌电图信号包含自愿和非自愿成分。我们假设运动的自愿和非自愿成分是不相关的，因此可以作为不同的协同肌肉活动模式来检测，并且去除非自愿成分将改善基于肌电图的在线控制。因此，我们对肌电图引导运动进行了基于协同作用的分解，并使用 Fitts 定律任务评估了哪些组件最具可控性。同样，我们还测试了哪些肌肉最具可控性。然后，我们测试了去除不可控组件或肌肉是否可以提高运动时间、成功率和吞吐量等方面的整体功能。我们发现，去除较不可控的组件或肌肉并没有改善肌电图控制性能，在许多情况下反而会降低性能。这些结果表明，运动障碍性脑瘫中的异常运动与普遍的自愿运动失真一致，而不是可分离的自愿和非自愿运动成分的叠加。
