http://www.jneuroengrehab.com/content/9/1/85/abstract
Abstract (provisional)
Background
sEMG signal has been widely used in different applications in kinesiology and rehabilitation
as well as in the control of human-machine interfaces. In general, the signals are
recorded with bipolar electrodes located in different muscles. However, such configuration
may disregard some aspects of the spatial distribution of the potentials like location
of innervation zones and the manifestation of inhomogineties in the control of the
muscular fibers. On the other hand, the spatial distribution of motor unit action
potentials has recently been assessed with activation maps obtained from High Density
EMG signals (HD-EMG), these lasts recorded with arrays of closely spaced electrodes.
The main objective of this work is to analyze patterns in the activation maps, associating
them with four movement directions at the elbow joint and with different strengths
of those tasks. Although the activation pattern can be assessed with bipolar electrodes,
HD-EMG maps could enable the extraction of features that depend on the spatial distribution
of the potentials and on the load-sharing between muscles, in order to have a better
differentiation between tasks and effort levels.
Methods
An experimental protocol consisting of isometric contractions at three levels of effort
during flexion, extension, supination and pronation at the elbow joint was designed
and HD-EMG signals were recorded with 2D electrode arrays on different upper-limb
muscles. Techniques for the identification and interpolation of artifacts are explained,
as well as a method for the segmentation of the activation areas. In addition, variables
related to the intensity and spatial distribution of the maps were obtained, as well
as variables associated to signal power of traditional single bipolar recordings.
Finally, statistical tests were applied in order to assess differences between information
extracted from single bipolar signals or from HD-EMG maps and to analyze differences
due to type of task and effort level.
Results
Significant differences were observed between EMG signal power obtained from single
bipolar configuration and HD-EMG and better results regarding the identification of
tasks and effort levels were obtained with the latter. Additionally, average maps
for a population of 12 subjects were obtained and differences in the co-activation
pattern of muscles were found not only from variables related to the intensity of
the maps but also to their spatial distribution.
Conclusions
Intensity and spatial distribution of HD-EMG maps could be useful in applications
where the identification of movement intention and its strength is needed, for example
in robotic-aided therapies or for devices like powered- prostheses or orthoses. Finally,
additional data transformations or other features are necessary in order to improve
the performance of tasks identification.
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