EMG burst presence probability: a joint time-frequency representation of muscle activity and its application to onset detection.
EMG burst presence probability: a joint time-frequency representation of muscle activity and its application to onset detection.
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DOI：
10.1016/j.jbiomech.2015.02.017
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发表时间：
2015-04-13
影响因子：
2.4
通讯作者：
Rymer WZ
中科院分区：
文献类型：
--
作者：
Liu J;Ying D;Rymer WZ
本研究的目的是量化时频域中的肌肉活动，从而提供测量肌肉活动的替代工具。本文提出了一种利用时频域肌电突发存在概率（EBPP）来测量肌肉活动的新方法。将肌电信号分为几个梅尔标度子带，并从每个子带中提取对数功率序列。每个对数功率序列可以被视为肌电突发和非突发状态之间转换的动态过程。采用隐马尔可夫模型 (HMM) 来阐述这一动态过程，因为 HMM 在建模 EMG 突发/非突发存在的时间相关性方面具有本质优势。 EBPP最终由HMM基于最大似然标准产生。我们的方法取得了与 Bonato 方法相当的性能。
The purpose of this study was to quantify muscle activity in the time-frequency domain, therefore providing an alternative tool to measure muscle activity. This paper presents a novel method to measure muscle activity by utilizing EMG burst presence probability (EBPP) in the time-frequency domain. The EMG signal is grouped into several Mel-scale subbands, and the logarithmic power sequence is extracted from each subband. Each log-power sequence can be regarded as a dynamic process that transits between the states of EMG burst and non-burst. The hidden Markov model (HMM) was employed to elaborate this dynamic process since HMM is intrinsically advantageous in modeling the temporal correlation of EMG burst/non-burst presence. The EBPP was eventually yielded by HMM based on the criterion of maximum likelihood. Our approach achieved comparable performance with the Bonato method.
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影响因子：
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