2 research outputs found
Feature Analysis for Classification of Physical Actions using surface EMG Data
Based on recent health statistics, there are several thousands of people with
limb disability and gait disorders that require a medical assistance. A robot
assisted rehabilitation therapy can help them recover and return to a normal
life. In this scenario, a successful methodology is to use the EMG signal based
information to control the support robotics. For this mechanism to function
properly, the EMG signal from the muscles has to be sensed and then the
biological motor intention has to be decoded and finally the resulting
information has to be communicated to the controller of the robot. An accurate
detection of the motor intention requires a pattern recognition based
categorical identification. Hence in this paper, we propose an improved
classification framework by identification of the relevant features that drive
the pattern recognition algorithm. Major contributions include a set of
modified spectral moment based features and another relevant inter-channel
correlation feature that contribute to an improved classification performance.
Next, we conducted a sensitivity analysis of the classification algorithm to
different EMG channels. Finally, the classifier performance is compared to that
of the other state-of the art algorithm
An Improved Compound Gaussian Model for Bivariate Surface EMG Signals Related to Strength Training
Recent literature suggests that the surface electromyography (sEMG) signals
have non-stationary statistical characteristics specifically due to random
nature of the covariance. Thus suitability of a statistical model for sEMG
signals is determined by the choice of an appropriate model for describing the
covariance. The purpose of this study is to propose a Compound-Gaussian (CG)
model for multivariate sEMG signals in which latent variable of covariance is
modeled as a random variable that follows an exponential model. The parameters
of the model are estimated using the iterative Expectation Maximization (EM)
algorithm. Further, a new dataset, electromyography analysis of human
activities database 2 (EMAHA-DB2) is developed. Based on the model fitting
analysis on the sEMG signals from EMAHA-DB2, it is found that the proposed CG
model fits more closely to the empirical pdf of sEMG signals than the existing
models. The proposed model is validated by visual inspection, further validated
by matching central moments and better quantitative metrics in comparison with
other models. The proposed compound model provides an improved fit to the
statistical behavior of sEMG signals. Further, the estimate of rate parameter
of the exponential model shows clear relation to the training weights. Finally,
the average signal power estimates of the channels shows distinctive dependency
on the training weights, the subject's training experience and the type of
activity.Comment: This article supersedes arXiv:2301.05417. This work has been
submitted to the IEEE for possible publication. Copyright may be transferred
without notice, after which this version may no longer be accessibl