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首页> 外文会议>Annual Conference of Japanese Society for Medical and Biological Engineering;Annual International Conference of the IEEE Engineering in Medicine and Biology Society >Pattern recognition control outperforms conventional myoelectric control in upper limb patients with targeted muscle reinnervation
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Pattern recognition control outperforms conventional myoelectric control in upper limb patients with targeted muscle reinnervation

机译：模式识别控制胜过有针对性的神经再支配的上肢患者常规的肌电控制

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摘要

Pattern recognition myoelectric control shows great promise as an alternative to conventional amplitude based control to control multiple degree of freedom prosthetic limbs. Many studies have reported pattern recognition classification error performances of less than 10% during offline tests; however, it remains unclear how this translates to real-time control performance. In this contribution, we compare the real-time control performances between pattern recognition and direct myoelectric control (a popular form of conventional amplitude control) for participants who had received targeted muscle reinnervation. The real-time performance was evaluated during three tasks; 1) a box and blocks task, 2) a clothespin relocation task, and 3) a block stacking task. Our results found that pattern recognition significantly outperformed direct control for all three performance tasks. Furthermore, it was found that pattern recognition was configured much quicker. The classification error of the pattern recognition systems used by the patients was found to be 16% ±(1.6%) suggesting that systems with this error rate may still provide excellent control. Finally, patients qualitatively preferred using pattern recognition control and reported the resulting control to be smoother and more consistent.
机译：模式识别肌电控制作为替代传统的基于幅度的控制多种自由度假肢的控制方法具有广阔的前景。许多研究报告了离线测试中模式识别分类错误的性能不到10％。但是，尚不清楚如何将其转化为实时控制性能。在这项贡献中，我们比较了接受目标肌肉神经支配的参与者在模式识别和直接肌电控制（传统的振幅控制的流行形式）之间的实时控制性能。在三个任务中评估了实时性能。 1）盒子和积木任务，2）晾衣夹重定位任务，和3）积木堆叠任务。我们的结果发现，模式识别在所有三个性能任务上均明显优于直接控制。此外，发现模式识别的配置要快得多。患者使用的模式识别系统的分类误差为16％±（1.6％），表明具有此误差率的系统仍可以提供出色的控制。最后，患者在质量上偏爱使用模式识别控件，并报告所得到的控件更加平滑和一致。

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