Application of Doppler radar for the recognition of hand gestures using optimized deep convolutional neural networks

In this paper, we investigate the optimal structure of deep convolutional neural networks for classifying human hand gestures using Doppler radar. When hand motions are captured by Doppler radar, the unique micro-Doppler signatures can be observed in the spectrogram. If the signature is distinguishable by a classifier, then the hand gesture can be used for controlling electronics and as an input modality for a human-computer interface. To classiiy signatures in the spectrogram, we propose the use of a deep convolutional neural network (DCNN) as a classifier. DCNN is a powerful classifier that extracts features as well as class boundaries through a training process. We measured seven hand gestures performed in front of Doppler radar while generating spectrograms. To identify an optimal structure, we trained several DCNNs by changing hyperparameters, such as the number of convolutional layers, the number of filters, and the filter size. The classification accuracy obtained from the optimal DCNN structure was approximately 87%.

I. Introduction

Recognition of hand gestures can offer interesting opportunities in the areas of electronics control, computer gaming, and defense [1]–[2]. By accepting hand gestures as an input modality, electronic devices can be controlled without touching a button. This capability is particularly useful for small electronics, in which embedding a button can be difficult. Moreover, it can heighten convenience for users.