Activity Detection from Wearable Electromyogram Sensors using Hidden Markov Model
This work addresses activity detection for healthcare and gesture analysis systems, but it is incremental as it applies an existing method (HMM) to sEMG data.
The paper tackled the problem of detecting activity regions in continuous surface electromyography (sEMG) signals for hand gestures, achieving an average accuracy of 96.25% for activity onsets and 87.5% for terminations.
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.