Department of Electronic Engineering, Gyeongsang National University, Jinju 52828, Republic of Korea.
Department of Electronic Engineering, Engineering Research Institute, Gyeongsang National University, Jinju 52828, Republic of Korea.
Sensors (Basel). 2024 Oct 21;24(20):6763. doi: 10.3390/s24206763.
In the field of gesture recognition technology, accurately detecting human gestures is crucial. In this research, ultrasonic transducers were utilized for gesture recognition. Due to the wide beamwidth of ultrasonic transducers, it is difficult to effectively distinguish between multiple objects within a single beam. However, they are effective at accurately identifying individual objects. To leverage this characteristic of the ultrasonic transducer as an advantage, this research involved constructing an ultrasonic array. This array was created by arranging eight transmitting transducers in a circular formation and placing a single receiving transducer at the center. Through this, a wide beam area was formed extensively, enabling the measurement of unrestricted movement of a single hand in the X, Y, and Z axes. Hand gesture data were collected at distances of 10 cm, 30 cm, 50 cm, 70 cm, and 90 cm from the array. The collected data were trained and tested using a customized Convolutional Neural Network (CNN) model, demonstrating high accuracy on raw data, which is most suitable for immediate interaction with computers. The proposed system achieved over 98% accuracy.
在手势识别技术领域，准确检测人体手势至关重要。本研究利用超声换能器进行手势识别。由于超声换能器的波束较宽，很难在单个波束内有效地区分多个物体。但是，它们在准确识别单个物体方面非常有效。为了利用超声换能器的这一特性作为优势，本研究涉及构建一个超声阵列。该阵列通过将八个发射换能器以圆形排列，并在中心放置一个单个接收换能器来创建。通过这种方式，形成了广泛的宽波束区域，能够测量单个手在 X、Y 和 Z 轴上的无限制运动。在距离阵列 10cm、30cm、50cm、70cm 和 90cm 的位置采集手势数据。使用定制的卷积神经网络 (CNN) 模型对采集到的数据进行训练和测试，该模型在原始数据上表现出很高的准确性，最适合与计算机进行即时交互。所提出的系统实现了超过 98%的准确率。
