Young Aaron J, Hargrove Levi J
Center for Bionic Medicine at the Rehabilitation Institute of Chicago, Chicago, IL 60611, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:3873-9. doi: 10.1109/IEMBS.2011.6090962.
Myoelectric pattern recognition control can potentially provide upper limb amputees with intuitive control of multiple prosthetic functions. However, the lack of robustness of myoelectric pattern recognition algorithms is a barrier for clinical implementation. One issue that can contribute to poor system performance is electrode shift, which is a change in the location of the electrodes with respect to the underlying muscles that occurs during donning and doffing and daily use. We investigated the effects of interelectrode distance and feature choice on system performance in the presence of electrode shift. Increasing the interelectrode distance from 2 cm to 4 cm significantly (p<0.01) improved classification accuracy in the presence of electrode shifts of up to 2 cm. In a controllability test, increasing the interelectrode distance from 2 cm to 4 cm improved the user's ability to control a virtual prosthesis in the presence of electrode shift. Use of an autoregressive feature set significantly (p<0.01) reduced sensitivity to electrode shift when compared to use of a traditional time-domain feature set.
肌电模式识别控制有潜力为上肢截肢者提供对多种假肢功能的直观控制。然而，肌电模式识别算法缺乏鲁棒性是临床应用的一个障碍。可能导致系统性能不佳的一个问题是电极移位，即在穿戴和脱下以及日常使用过程中电极相对于其下方肌肉的位置发生变化。我们研究了在存在电极移位的情况下电极间距和特征选择对系统性能的影响。在电极移位达2厘米的情况下，将电极间距从2厘米增加到4厘米显著（p<0.01）提高了分类准确率。在可控性测试中，在存在电极移位的情况下，将电极间距从2厘米增加到4厘米提高了用户控制虚拟假肢的能力。与使用传统时域特征集相比，使用自回归特征集显著（p<0.01）降低了对电极移位的敏感性。
