Finger gesture recognition with smart skin technology and deep learning

Finger gesture recognition was extensively studied in recent years for a
wide range of human-machine interface applications. Surface
electromyography (sEMG), in particular, is an attractive, enabling
technique in the realm of finger gesture recognition, and both low and
high-density sEMG were previously studied. Despite the clear potential,
cumbersome electrode wiring and electronic instrumentation render
contemporary sEMG-based finger gestures recognition to be performed under
unnatural conditions. Recent developments in smart skin technology provide
an opportunity to collect sEMG data in more natural conditions. Here we
report on a novel approach based on a soft 16-electrode array, a miniature
and wireless data acquisition unit and neural network analysis, in order
to achieve gesture recognition under natural conditions. Finger gesture
recognition accuracy values, as high as 93.1%, were achieved for 8
gestures when the training and test data were from the same session. For
the first time, high accuracy values are also reported for training and
test data from different sessions for three different hand positions.
These results demonstrate an important step towards sEMG-based gesture
recognition in non-laboratory settings, such as in gaming or Metaverse.