Identification of motion intention and muscle activation strategy is necessary to control human–machine
interfaces like prostheses or orthoses, as well as other rehabilitation devices, games and computer-based
training programs. Pattern recognition from sEMG signals has been extensively investigated in the last
decades, however, most of the studies did not take into account different strengths and EMG distributions
associated to the intended task. The identification of such quantities could be beneficial for the training of
the subject or the control of assistive devices. Recent studies have shown the need to improve patternrecognition
classification by reducing sensitivity to changes in the exerted strength, muscle-electrode
shifts and bad contacts. Surface High Density EMG (HD-EMG) obtained from 2-dimensional arrays can
provide much more information than electrode pairs for inferring not only motion intention but also
the strategy adopted to distribute the load between muscles as well as changes in the spatial distribution
of motor unit action potentials within a single muscle because of it.
The objectives of this study were: (a) the automatic identification of four isometric motor tasks associated
with the degrees of freedom of the forearm: flexion–extension and supination–pronation and (b)
the differentiation among levels of voluntary contraction at low-medium efforts. For this purpose, monopolar
HD-EMG maps were obtained from five muscles of the upper-limb in healthy subjects. An original
classifier is proposed, based on: (1) Two steps linear discriminant analysis of the EMG information for
each type of contraction, and (2) features extracted from HD-EMG maps and related to its intensity
and distribution in the 2D space. The classifier was trained and tested with different effort levels. Spatial
distribution-based features by themselves are not sufficient to classify the type of task or the effort level
with an acceptable accuracy; however, when calculated with the ‘‘isolated masses’’ method proposed in
this study and combined with intensity-base features, the performance of the classifier is improved. The
classifier is capable of identifying the tasks even at 10% of Maximum Voluntary Contraction, in the range
of effort level developed by patients with neuromuscular disorders, showing that intention end effort of
motion can be estimated from HD-EMG maps and applied in rehabilitation.
