Real-time classification of forearm electromyographic signals corresponding to user-selected intentional movements for multifunction prosthesis control
Real-time classification of forearm electromyographic signals corresponding to user-selected intentional movements for multifunction prosthesis control
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DOI：
10.1109/tnsre.2007.908376
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发表时间：
2007-12-01
影响因子：
4.9
通讯作者：
Chau, Tom
中科院分区：
文献类型：
--
作者：
Momen, Kaveh;Krishnan, Sridhar;Chau, Tom
基于模式识别的多功能假肢控制策略已在很大程度上被证明与典型的健全的手运动的子集。这些运动对于截肢者来说通常是不自然的，需要大量的用户训练，并且不能最大限度地利用剩余肌肉活动的潜力。本文提出了一种实时肌电图（EMG）分类用户选择的有意运动，而不是强加的标准运动的子集。肌电图信号记录从前臂伸肌和屈肌的七个健全的参与者和一个先天性截肢者。参与者通过独特的训练方案自由选择和标记自己的肌肉收缩。信号通过均方根值的自然对数进行参数化，在0.2 s滑动和非重叠窗口内计算。利用模糊C均值聚类对特征空间进行分割。仅用来自每个用户的2分钟训练数据，分类器就以92.7% +/-3.2%的平均准确率区分了四种不同的动作。这种准确性可以通过额外的训练数据和实践中提高的用户熟练度来进一步提高。所提出的方法可以促进动态上肢假肢控制策略的发展，使用任意的，用户偏好的肌肉收缩。
Pattern recognition-based multifunction prosthesis control strategies have largely been demonstrated with subsets of typical able-bodied hand movements. These movements are often unnatural to the amputee, necessitating significant user training and do not maximally exploit the potential of residual muscle activity. This paper presents a real-time electromyography (EMG) classifier of user-selected intentional movements rather than an imposed subset of standard movements. EMG signals were recorded from the forearm extensor and flexor muscles of seven able-bodied participants and one congenital amputee. Participants freely selected and labeled their own muscle contractions through a unique training protocol. Signals were parameterized by the natural logarithm of root mean square values, calculated within 0.2 s sliding and non overlapping windows. The feature space was segmented using fuzzy C-means clustering. With only 2 min of training data from each user, the classifier discriminated four different movements with an average accuracy of 92.7% +/- 3.2%. This accuracy could be further increased with additional training data and improved user proficiency that comes with practice. The proposed method may facilitate the development of dynamic upper extremity prosthesis control strategies using arbitrary, user-preferred muscle contractions.