title: Robust muscle activity onset detection using an unsupervised electromyogram learning framework.
abstract: Accurate muscle activity onset detection is an essential prerequisite for many applications of surface electromyogram (EMG). This study presents an unsupervised EMG learning framework based on a sequential Gaussian mixture model (GMM) to detect muscle activity onsets. The distribution of the logarithmic power of EMG signal was characterized by a two-component GMM in each frequency band, in which the two components respectively correspond to the posterior distribution of EMG burst and non-burst logarithmic powers. The parameter set of the GMM was sequentially estimated based on maximum likelihood, subject to constraints derived from the relationship between EMG burst and non-burst distributions. An optimal threshold for EMG burst/non-burst classification was determined using the GMM at each frequency band, and the final decision was obtained by a voting procedure. The proposed novel framework was applied to simulated and experimental surface EMG signals for muscle activity onset detection. Compared with conventional approaches, it demonstrated robust performance for low and changing signal to noise ratios in a dynamic environment. The framework is applicable for real-time implementation, and does not require the assumption of non EMG burst in the initial stage. Such features facilitate its practical application.
authors.affiliation: Sensory Motor Performance Program, Rehabilitation Institute of Chicago, Chicago, United States of America.
authors.forename: Jie
authors.initials: J
authors.lastname: Liu
authors.affiliation: Institute of Acoustics, Chinese Academy of Sciences, Beijing, China.
authors.forename: Dongwen
authors.initials: D
authors.lastname: Ying
authors.affiliation: Sensory Motor Performance Program, Rehabilitation Institute of Chicago, Chicago, United States of America; Department of Physical Medicine & Rehabilitation, Northwestern University, Chicago, United States of America.
authors.forename: William Z
authors.initials: WZ
authors.lastname: Rymer
authors.affiliation: Department of Physical Medicine & Rehabilitation, University of Texas Health Science Center at Houston; and TIRR Memorial Hermann Research Center, Houston, United States of America; Biomedical Engineering Program, University of Science and Technology of China, Hefei, China.
authors.forename: Ping
authors.initials: P
authors.lastname: Zhou
mesh_terms: D004576:Electromyography / Q000379:methods*; D006801:Humans; D000069550:Machine Learning; D018482:Muscle, Skeletal / Q000502:physiology*; D012815:Signal Processing, Computer-Assisted