Objective Evaluation of Clutter Suppression for Micro-Doppler Spectrograms of Hand Gesture/Sign Language Based on Pseudo-Reference Image

Abstract

Gesture and sign language recognition technology enables machines to understand the meaning of human hand movements. In human-computer interaction, it is expected that gesture/sign language recognition technologies will overcome equipment size and application environment constraints; in information communication, gesture/sign language recognition technology will assist healthy people in more easily entering the world of deaf people and better understanding and meeting their inner emotional needs; and in patient monitoring, gesture/sign language recognition technologies will detect abnormal behavior in the elderly or patients and reduce possible safety issues. As a result, it has significant implications for both research and broad application. Because the radar sensor can work normally in a wide range of illumination and weather conditions, as well as penetrate the shelter to receive the moving object echo signal and preserve individual privacy, it is becoming increasingly popular in a variety of recognition tasks. A primary step in using radar sensors for gesture/sign language recognition is to suppress clutter to highlight useful motion information. To evaluate the clutter suppression effect, however, an objective metric is required. We present an objective assessment metric based on the pseudo-reference image and an automatic threshold selection method based on Otsu for clutter suppression, as well as subjective and objective experiments demonstrating their effectiveness and universality in gesture/sign language recognition. Notably, our proposed metric can be used for any recognition task that uses micro-Doppler spectrograms as the dataset.