High energy spectrogram with integrated prior knowledge for EMG-based locomotion classification
High energy spectrogram with integrated prior knowledge for EMG-based locomotion classification
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DOI：
10.1016/j.medengphy.2015.03.001
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发表时间：
2015-05-01
影响因子：
2.2
通讯作者：
Hahn, Michael E.
中科院分区：
文献类型：
--
作者：
Joshi, Deepak;Nakamura, Bryson H.;Hahn, Michael E.
肌电图（EMG）信号表示在特定于运动和过渡的分类应用中至关重要。对于给定的信号，可以使用判别函数或if-else规则集、使用从训练示例中导出的学习算法来执行分类。在目前的工作中，基于频谱图的方法来分类（EMG）信号的运动模式。计算每块肌肉的频谱图并求和以形成直方图。If-else规则用于基于匹配分数对测试数据进行分类。运动类型的先验知识减少了类空间的排他性运动模式。采集了健康受试者在地面行走（W）、上楼梯（SA）和上下楼梯过渡（W-SA）时的7块腿部肌肉的肌电图数据。从原始数据集中删除三块具有最小辨别力的肌肉，以检查对分类准确性的影响。初始分类错误为
Electromyogram (EMG) signal representation is crucial in classification applications specific to locomotion and transitions. For a given signal, classification can be performed using discriminant functions or if-else rule sets, using learning algorithms derived from training examples. In the present work, a spectrogram based approach was developed to classify (EMG) signals for locomotion mode. Spectrograms for each muscle were calculated and summed to develop a histogram. If-else rules were used to classify test data based on a matching score. Prior knowledge of locomotion type reduced class space to exclusive locomotion modes. The EMG data were collected from seven leg muscles in a sample of able-bodied subjects while walking over ground (W), ascending stairs (SA) and the transition between (W-SA). Three muscles with least discriminating power were removed from the original data set to examine the effect on classification accuracy. Initial classification error was