EMG burst presence probability: a joint time-frequency representation of muscle activity and its application to onset detection.
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.
Sensory Motor Performance Program, Rehabilitation Institute of Chicago, USA. Electronic address: jie.liu2009@gmail.com.
Jie
J
Liu
Institute of Acoustics, Chinese Academy of Sciences, Beijing, China.
Dongwen
D
Ying
Sensory Motor Performance Program, Rehabilitation Institute of Chicago, USA; Department of Physical Medicine & Rehabilitation, Northwestern University, Chicago, USA.
William Zev
WZ
Rymer
D000465:Algorithms; D004576:Electromyography / Q000379:methods*; D006801:Humans; D009119:Muscle Contraction; D018482:Muscle, Skeletal / Q000502:physiology; D011336:Probability; D012815:Signal Processing, Computer-Assisted
EMG burst presence probability (EBPP); EMG onset; Electromyography (EMG); Hidden Markov model (HMM)