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We developed an impulse radio ultra-wideband (IR-UWB) radar-based system that can recognize alphanumeric characters in midair without the need for any handheld device. The hardware consists of four IR-UWB radar sensors set up with a rectangular geometry. Writing a single character in midair results in artifacts that make some characters look similar on a position trajectory-based (x, y) plane, which makes them difficult to classify. Thus, we developed an algorithm that transforms 2D coordinate image data into trigonometric ratios (i.e., tangents) and plots them against the time axis to obtain unique images for training a convolutional neural network. An extended Kalman filter is used to obtain the 2D trajectories of hand motions. To evaluate our proposed method, we first applied it to characters that may be written in midair very simply without creating artifacts and compared its performance with that of a state-of-the-art digit classification algorithm. Then, we considered combining characters written midair with and without artifacts. After the individual character recognition, we combined the characters into words. We defined a specific marker based on an energy threshold to detect the start and end of a character for midair writing. The energy level was found to change drastically when the hand is pulled in and out of the radar plane. The proposed method was found to outperform the current state of the art at character classification when artifacts are present in the images.

In-Air Continuous Writing Using UWB Impulse Radar Sensors