  Electromyography (EMG) data has been extensively adopted as an intuitive
interface for instructing human-robot collaboration. A major challenge of the
real-time detection of human grasp intent is the identification of dynamic EMG
from hand movements. Previous studies mainly implemented steady-state EMG
classification with a small number of grasp patterns on dynamic situations,
which are insufficient to generate differentiated control regarding the
muscular activity variation in practice. In order to better detect dynamic
movements, more EMG variability could be integrated into the model. However,
only limited research were concentrated on such detection of dynamic grasp
motions, and most existing assessments on non-static EMG classification either
require supervised ground-truth timestamps of the movement status, or only
contain limited kinematic variations. In this study, we propose a framework for
classifying dynamic EMG signals into gestures, and examine the impact of
different movement phases, using an unsupervised method to segment and label
the action transitions. We collected and utilized data from large gesture
vocabularies with multiple dynamic actions to encode the transitions from one
grasp intent to another based on common sequences of the grasp movements. The
classifier for identifying the gesture label was constructed afterwards based
on the dynamic EMG signal, with no supervised annotation of kinematic movements
required. Finally, we evaluated the performances of several training strategies
using EMG data from different movement phases, and explored the information
revealed from each phase. All experiments were evaluated in a real-time style
with the performance transitions over time presented.
