Study on Interaction Between Temporal and Spatial Information in Classification of EMG Signals for Myoelectric Prostheses
Study on Interaction Between Temporal and Spatial Information in Classification of EMG Signals for Myoelectric Prostheses
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DOI：
10.1109/tnsre.2017.2687761
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发表时间：
2017-10-01
影响因子：
4.9
通讯作者：
Soraghan, John J.
中科院分区：
文献类型：
--
作者：
Menon, Radhika;Di Caterina, Gaetano;Soraghan, John J.
先进的前臂假肢设备采用分类器来识别不同的肌电图（EMG）信号模式，以便识别用户的预期运动姿势。分类精度是假肢的实时可控性的主要决定因素之一，因此有必要实现尽可能高的精度。在本文中，我们研究的时间和空间信息提供给分类器的离线性能的影响，并分析它们之间的相互依赖性。肌电图数据与七个实际的手势记录从部分手和经桡截肢志愿者以及健全的志愿者。进行了广泛的调查，研究分析窗口长度、窗口重叠和电极通道数量对分类准确度的影响及其相互作用。我们的主要发现是，分析窗口长度对分类精度的影响实际上与所有参与者组的电极数量无关;窗口重叠对分类器性能没有直接影响，与窗口长度、通道数量或肢体状况无关;肢体缺陷的类型和现有通道数影响通过增加更多通道数实现的分类误差的减少;部分手截肢者优于经桡动脉截肢者，其分类准确度仅比健全志愿者的值低11.3%。
Advanced forearm prosthetic devices employ classifiers to recognize different electromyography (EMG) signal patterns, in order to identify the user's intended motion gesture. The classification accuracy is one of the main determinants of real-time controllability of a prosthetic limb and hence the necessity to achieve as high an accuracy as possible. In this paper, we study the effects of the temporal and spatial information provided to the classifier on its off-line performance and analyze their inter-dependencies. EMG data associated with seven practical hand gestures were recorded from partial-hand and trans-radial amputee volunteers as well as able-bodied volunteers. An extensive investigation was conducted to study the effect of analysis window length, window overlap, and the number of electrode channels on the classification accuracy as well as their interactions. Our main discoveries are that the effect of analysis window length on classification accuracy is practically independent of the number of electrodes for all participant groups; window overlap has no direct influence on classifier performance, irrespective of the window length, number of channels, or limb condition; the type of limb deficiency and the existing channel count influence the reduction in classification error achieved by adding more number of channels; partial-hand amputees outperform trans-radial amputees, with classification accuracies of only 11.3% below values achieved by able-bodied volunteers.