计算机科学
手势
频道(广播)
手势识别
领域(数学分析)
语音识别
人工智能
领域(数学)
无线
方向(向量空间)
计算机视觉
计算机网络
电信
数学
数学分析
纯数学
几何学
标识
DOI:10.1145/3503047.3503148
摘要
With the rapid development of wireless sensing, intelligent human-computer interaction, and other fields, gesture recognition based on WiFi has become an important research field. Gesture recognition based on WiFi has the advantages of non-contact and privacy protection. In addition, the use of home WiFi makes the technology have a broad application scenario. At present, most gesture recognition models based on WiFi can only achieve good results in a specific domain. When changing the environment or the orientation of gesture action, the performance of the model becomes very poor. This paper proposes a gesture recognition system based on the channel attention mechanism and CNN-LSTM fusion model. On the one hand, the channel attention mechanism can consider the importance of different channel characteristics; On the other hand, the CNN-LSTM fusion model can extract richer features in the time domain and space domain. The system has achieved good classification results in multiple domains of the public data set widar3.0.
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