A subject-independent method for automatically grading electromyographic features during a fatiguing contraction.
A subject-independent method for automatically grading electromyographic features during a fatiguing contraction.
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DOI：
10.1109/tbme.2012.2193881
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发表时间：
2012-06
期刊：
影响因子：
--
通讯作者：
Panchanathan S
中科院分区：
文献类型：
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作者：
Chattopadhyay R;Jesunathadas M;Poston B;Santello M;Ye J;Panchanathan S
许多研究试图通过肌电图 (EMG) 信号来监测疲劳。然而，疲劳会以特定对象的方式影响肌电图。我们在这里提出了一个独立于主题的框架，用于基于主成分分析和因子分析来监测伴随肌肉疲劳的肌电图特征的变化。所提出的框架基于多个时域和频域特征，这与大多数基于两到三个特征的现有工作不同。结果表明，从这些特征的因子分析中获得的潜在因子提供了一个稳健且统一的框架。该框架从形成参考组的多个受试者的 EMG 信号中学习模型，并以从 0 到 1 的等级监测测试受试者持续次最大收缩期间 EMG 特征的变化。该框架针对从 8 名健康受试者的 12 块肌肉收集的 EMG 信号进行了测试。当映射到框架上时，测试对象的因子得分分布对于特定于主题和独立于主题的情况都是相似的。
Many studies have attempted to monitor fatigue from electromyogram (EMG) signals. However, fatigue affects EMG in a subject-specific manner. We present here a subject-independent framework for monitoring the changes in EMG features that accompany muscle fatigue based on principal component analysis and factor analysis. The proposed framework is based on several time- and frequency-domain features, unlike most of the existing work, which is based on two to three features. Results show that latent factors obtained from factor analysis on these features provide a robust and unified framework. This framework learns a model from EMG signals of multiple subjects, that form a reference group, and monitors the changes in EMG features during a sustained submaximal contraction on a test subject on a scale from zero to one. The framework was tested on EMG signals collected from 12 muscles of eight healthy subjects. The distribution of factor scores of the test subject, when mapped onto the framework was similar for both the subject-specific and subject-independent cases.