Abstract: With the rapid development of intelligent sensing technology,the field of human computer interaction(HCI) has entered a new era.Traditional HCI methods,predominantly reliant on wearable devices and cameras to collect user behavior data,have significant limitations despite their precise recognition capabilities.Wearable devices,for instance,impose additional burden on users,whereas camera-based solutions are susceptible to ambient lighting conditions and pose significant privacy concerns.These challenges considerably restrict their applicability in daily life.To solve these challenges,we utilize the exceptional sensiti-vity and spatial resolution of impulse radio ultra-wideband(IR-UWB) in the field of radio frequency(RF) to propose a novel and contact-free method for human motion recognition based on a dual-stream fusion network.This method adeptly captures the temporal signal variations caused by target movements and extracts the corresponding frequency-domain features by analyzing Doppler frequency shift(DFS) changes on the time-domain signals.Subsequently,a sophisticated dual-stream network model,integrating multi-dimensional convolutional neural networks(CNNs) and GoogLeNet modules,is developed to facilitate precise action recognition.Through extensive experimental tests,the results show that the proposed method achieves an average accuracy of 94.89% for eight common daily human actions and maintains an accuracy of over 90% under varying test conditions,thereby va-lidating the robustness of the proposed method.

Key words: Human computer interaction, Wireless sensing, Impulse radio ultra-wideband(IR-UWB), Motion recognition