  The electromyography (EMG) signals have been widely utilized in human robot
interaction for extracting user hand and arm motion instructions. A major
challenge of the online interaction with robots is the reliable EMG recognition
from real-time data. However, previous studies mainly focused on using
steady-state EMG signals with a small number of grasp patterns to implement
classification algorithms, which is insufficient to generate robust control
regarding the dynamic muscular activity variation in practice. Introducing more
EMG variability during training and validation could implement a better
dynamic-motion detection, but only limited research focused on such
grasp-movement identification, and all of those assessments on the non-static
EMG classification require supervised ground-truth label of the movement
status. In this study, we propose a framework for classifying EMG signals
generated from continuous grasp movements with variations on dynamic arm/hand
postures, using an unsupervised motion status segmentation method. We collected
data from large gesture vocabularies with multiple dynamic motion phases to
encode the transitions from one intent to another based on common sequences of
the grasp movements. Two classifiers were constructed for identifying the
motion-phase label and grasp-type label, where the dynamic motion phases were
segmented and labeled in an unsupervised manner. The proposed framework was
evaluated in real-time with the accuracy variation over time presented, which
was shown to be efficient due to the high degree of freedom of the EMG data.
