  Surface electromyography (sEMG) is becoming exceeding useful in applications
involving analysis of human motion such as in human-machine interface,
assistive technology, healthcare and prosthetic development. The proposed work
presents a novel dual stage classification approach for classification of
grasping gestures from sEMG signals. A statistical assessment of these
activities is presented to determine the similar characteristics between the
considered activities. Similar activities are grouped together. In the first
stage of classification, an activity is identified as belonging to a group,
which is then further classified as one of the activities within the group in
the second stage of classification. The performance of the proposed approach is
compared to the conventional single stage classification approach in terms of
classification accuracies. The classification accuracies obtained using the
proposed dual stage classification are significantly higher as compared to that
for single stage classification.
