  Surface electromyography (sEMG) has gained significant importance during
recent advancements in consumer electronics for healthcare systems, gesture
analysis and recognition and sign language communication. For such a system, it
is imperative to determine the regions of activity in a continuously recorded
sEMG signal. The proposed work provides a novel activity detection approach
based on Hidden Markov Models (HMM) using sEMG signals recorded when various
hand gestures are performed. Detection procedure is designed based on a
probabilistic outlook by making use of mathematical models. The requirement of
a threshold for activity detection is obviated making it subject and activity
independent. Correctness of the predicted outputs is asserted by classifying
the signal segments around the detected transition regions as activity or rest.
Classified outputs are compared with the transition regions in a stimulus given
to the subject to perform the activity. The activity onsets are detected with
an average of 96.25% accuracy whereas the activity termination regions with an
average of 87.5% accuracy with the considered set of six activities and four
subjects.
