WiFi-based gesture recognition plays an important role in touchless human-computer interaction.However,existing WiFi-based gesture recognition systems faced the challenges of small data amount and poor cross-domain performance.In order to solve the above problems,the captured raw WiFi channel state information(CSI)is denoised by CSI Ratio,the extracted phase and converted into CSI images,which is transformed into an image classification problem.Then the transformed images are fed into the prototypical network(PN)for small sample cross-domain gesture recognition,and an enhanced Convolutional Block Attention Module(CSI-CBAM)is added to the PN feature extraction network to improve the gesture representation learn-ing.Extensive experiments were conducted on the Widar3.0 dataset.The experimental results showed that when each class in support set reaches four labeled samples,the system average recognition accuracies are 93.54%,91.28%,91.99%,and 89.16%for cross-environment,cross-user,cross-location,and cross-orientation,respectively.Average cross-domain accu-racy is higher than 90%,the proposed method only required a small number of labeled samples to achieve high accuracy cross-domain recognition.
关键词
手势识别/信道状态信息/人机交互/图像分类/注意力机制
Key words
gesture recognition/channel state information/human-computer interaction/image classification/attention mechanism
维普
万方数据