Robust muscle activity onset detection using an unsupervised electromyogram learning framework.
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 logarit...
Main Authors: | Jie Liu, Dongwen Ying, William Z Rymer, Ping Zhou |
---|---|
Format: | Article |
Language: | English |
Published: |
Public Library of Science (PLoS)
2015-01-01
|
Series: | PLoS ONE |
Online Access: | http://europepmc.org/articles/PMC4454555?pdf=render |
Similar Items
-
PROLIFIC: A Fast and Robust Profile-Likelihood-Based Muscle Onset Detection in Electromyogram Using Discrete Fibonacci Search
by: Easter S. Suviseshamuthu, et al.
Published: (2020-01-01) -
Event Detection of Muscle Activation Using an Electromyogram
by: Kimoon Kang, et al.
Published: (2020-08-01) -
Stress Detection using Electromyogram Signal of Erector Spinae Muscles
by: Sara Pourmohammadi, et al.
Published: (2018-05-01) -
autotuned electrical muscle stimulator with electromyogram biofeedback
by: Nazali, Muhammad Naqiuddin, et al.
Published: (2017) -
Normalisation of electromyograms
by: Burden, Adrian M.
Published: (2002)