부산대학교 도서관

Device-free gesture recognition has attracted significant attention due to its potential applications in pervasive interaction. It enables gesture recognition in a device-free and contact-free manner by analyzing the influence pattern of human gestures on surrounding wireless signals, such as mmWave signals. Although remarkable progress has been achieved in this area, the recognition performance will degrade remarkably when gestures are conducted in different scenarios. In this article, we leverage mmWave signals to design two robust trajectory features, i.e., the trajectory image and the trajectory time-sequence features, that are independent of the conducted scenarios to solve the aforementioned problems. Specifically, we employ the particle filter algorithm to construct the raw trajectory image utilizing range measurements, rotate and enhance the image to obtain the trajectory image feature suitable for recognition by leveraging a public handwriting font image data set as the training set. Additionally, we derive the range of the trajectory relative to a stable point as the trajectory time-sequence feature. With these trajectory features, we design a deep network to perform the gesture recognition task. To validate the effectiveness of the proposed methods, we conduct extensive experiments on a 77GHz mmWave testbed. The results indicate that the two proposed trajectory features are feasible for achieving scenario-independent gesture recognition.