Abstract—The evaluation of the Postural Control System (PCS)
has applications in rehabilitation, sports medicine, gait analysis,
fall detection, and diagnosis of many diseases associated with
a reduction in balance ability. Standing involves significant
muscle use to maintain balance, making standing balance a good
indicator of the health of the PCS. Inertial sensor systems have
been used to quantify standing balance by assessing displacement
of the Center Of Mass (COM), resulting in several standardized
measures. Electromyogram (EMG) sensors directly measure the
muscle control signals. Despite strong evidence of the potential of
muscle activity for balance evaluation, less study has been done on
extracting unique features from EMG data that express balance
abnormalities. In this paper, we present machine learning and
statistical techniques to extract parameters from EMG sensors
placed on the Tibialis anterior and Gastrocnemius muscles
which show a strong correlation to the standard parameters
extracted from accelerometer data. This novel interpretation of
the neuromuscular system provides a unique method of assessing
human balance based on EMG signals. In order to verify the
effectiveness of the introduced features in measuring postural
sway, we conduct several classification tests that operate on
the EMG features and predict significance of different balance
measures.