Zhang Zhenyi, Hao Zhanjun, Li Mengqiao
School of Computer Science and Engineering, Northwest Normal University, Lanzhou, China.
Gansu Province Internet of Things Engineering Research Center, Lanzhou, 730070, China.
Sci Rep. 2025 Mar 20;15(1):9654. doi: 10.1038/s41598-025-93837-1.
With the rapid development of smart devices and their applications, using mobile devices for human-computer interaction has become important. Recent work uses ultrasound to perceive gestures. However, it is difficult to represent the time and frequency information of gestures, and, they are often classified as combined actions in continuous multiple gestures. We propose the Doppler Object Detection Algorithm (DODA) to decouple the information from each gesture's time and frequency domain in continuous gestures and output the gesture classifications. DODA thus maps the feature information of multiple gestures from Doppler frequency shift images to information about real gesture actions. We present time domain Intersection over Union (Tiou), which computes the Tiou between each adjacent gesture to obtain more accurate prediction fields. We use the static exception eliminate algorithm (SEEA) to eliminate the effects of frame activity anomalies and use the mapping relationship of the DODA algorithm for data enhancement. We design an UltraWX to deploy on any mobile device. Our experimental results show that UltraWX can effectively segment and recognize continuous gestures, and output the start and end time of each gesture, and UltraWX can achieve 93.6% recognition accuracy for continuous gestures in complex environments.
随着智能设备及其应用的快速发展，使用移动设备进行人机交互变得越来越重要。最近的研究工作利用超声波来感知手势。然而，难以表示手势的时间和频率信息，并且在连续多个手势中它们经常被分类为组合动作。我们提出了多普勒目标检测算法（DODA），以在连续手势中从每个手势的时域和频域解耦信息，并输出手势分类。因此，DODA将多个手势的特征信息从多普勒频移图像映射到有关实际手势动作的信息。我们提出了时域交并比（Tiou），它计算每个相邻手势之间的Tiou以获得更准确的预测字段。我们使用静态异常消除算法（SEEA）来消除帧活动异常的影响，并使用DODA算法的映射关系进行数据增强。我们设计了一个UltraWX以部署在任何移动设备上。我们的实验结果表明，UltraWX可以有效地分割和识别连续手势，并输出每个手势的开始和结束时间，并且UltraWX在复杂环境中对连续手势的识别准确率可以达到93.6%。
