Sign Language/Gesture Recognition Based on Cumulative Distribution Density Features Using UWB Radar

It is an important topic to effectively perceive the gestures of the elderly and patients, communicate conveniently with the deaf and dumb, and accurately transmit abnormal alarm information through the network, which would have vast applications in the future. Seamlessly communicating among people, including vulnerable groups, is an important sign of social civilization and technological progress. As an important sensor, micro-Doppler (MD) radar has many advantages such as not being restricted by ambient light, long detection distance, no need to consider occlusion, and sensitive detection to slight movements. In recent years, it has been used in various recognition tasks. In this article, we proposed a sign language (SL)/hand gesture recognition method based on a novel discriminative feature, built a measurement system of hand movements using an ultrawideband (UWB) radar, measured ten-type, 15-type SL actions, and ten-type hand gesture actions, and completed the tasks of ten-type SL/hand gesture recognition based on the extracted new features. Compared with the five related methods, experimental results show that the proposed method improves the recognition accuracy by 6.4% on the ten-type SL dataset, which proves the effectiveness of the new cumulative distribution density (CDD) features. For comparison, the proposed method is directly applied to the ten-type hand gesture dataset without any change, and recognition accuracy is improved by 8.6%, which verifies the universality of the proposed new CDD features. Meanwhile, we also give the experimental results when the radial distance measured by radar changes and the types of SL increase.