Hand gesture-based interface with multichannel sEMG band enabling unknown gesture discrimination

A forearm band consisting of 7 EMG sensors was developed. The band is dedicated to serve as a human-machine interface and its applicability as a gesture-based interface was presented. The gesture recognition was performed by ANN with softmax output function. The classifier uses output entropy function to discriminate between known command gestures and unknown gestures. 15 features of low computational complexity were selected. The preliminary test was performed for 4 healthy volunteers. Continuous time series were used, including unknown gestures and transitions. Special attention was paid to analyze system properties of detecting and rejecting unknown gestures (not commands movements), even if they were not included in training set. Obtained preliminary results show the sensitivity for command gestures (including transition phases) of 97% and fall out of 1.9%. In case of the system trained to reject unknown gestures the sensitivity and fall out were 82% and 9%, respectively. The calculated unknown gestures rejection rate was at 96%. It was shown that in most cases the interface is robust to false positive detection of command gestures while performing other gestures, even if the same muscles groups were recruited for movement.