Recognizing unseen gym activities from streaming data – Accelerometer Vs. Electromyogram

Abstract

Wearable sensors based activity recognition is a research area where mostly inertial measurement unit based information is used to recognize human activities. Commonly the approaches are based on accelerometer data while in this study the potential of electromyogram signals in activity recognition is studied. The actual research problem tackled is one of themajor drawbacks in activity recognition, namely to add completely new activities in real life to the recognition models. In this study, it was shown that in gym settings electromyogram signals clearly outperforms the accelerometer data in recognition of completely new sets of gym movements from streaming data even though the sensors would not be positioned directly to the muscles trained.