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.
