  In this paper, we investigate novel data collection and training techniques
towards improving classification accuracy of non-moving (static) hand gestures
using a convolutional neural network (CNN) and
frequency-modulated-continuous-wave (FMCW) millimeter-wave (mmWave) radars.
Recently, non-contact hand pose and static gesture recognition have received
considerable attention in many applications ranging from human-computer
interaction (HCI), augmented/virtual reality (AR/VR), and even therapeutic
range of motion for medical applications. While most current solutions rely on
optical or depth cameras, these methods require ideal lighting and temperature
conditions. mmWave radar devices have recently emerged as a promising
alternative offering low-cost system-on-chip sensors whose output signals
contain precise spatial information even in non-ideal imaging conditions.
Additionally, deep convolutional neural networks have been employed extensively
in image recognition by learning both feature extraction and classification
simultaneously. However, little work has been done towards static gesture
recognition using mmWave radars and CNNs due to the difficulty involved in
extracting meaningful features from the radar return signal, and the results
are inferior compared with dynamic gesture classification. This article
presents an efficient data collection approach and a novel technique for deep
CNN training by introducing ``sterile'' images which aid in distinguishing
distinct features among the static gestures and subsequently improve the
classification accuracy. Applying the proposed data collection and training
methods yields an increase in classification rate of static hand gestures from
$85\%$ to $93\%$ and $90\%$ to $95\%$ for range and range-angle profiles,
respectively.
