随着智能感知技术的飞速发展,人机交互(Human Computer Interaction,HCI)领域迎来了全新的发展态势.传统的人机交互方法主要依赖可穿戴设备或者摄像头采集用户的行为数据,虽然识别精准,却存在不小的局限性.具体而言,可穿戴设备会给用户带来额外的使用负担,而基于摄像头的方案不仅会受到环境光线的影响,还会涉及用户隐私的泄露,这些因素均限制了其在日常生活中的广泛应用.为了突破这些限制,实现精确的、非接触式人机交互应用,利用无线射频(Radio Frequency,RF)领域中脉冲超宽带(Impulse Radio Ultra-Wideband,IR-UWB)所具有的高灵敏度和精细空间分辨率等优势,提出了一种基于双流融合网络的非接触式人体动作识别方法.该方法捕获目标运动所导致的时域信号变化,并通过对时域特征进行多普勒频移变化,提取到对应的频域特征.在此基础上,构建了一个融合多维卷积神经网络(Convolutional Neural Net-works,CNNs)和GoogLeNet模块的双流网络模型,以实现高精度的动作识别.通过广泛的实验测试,结果表明所提方法对8种常见人体动作的平均识别准确率达到94.89％,并且在不同的测试条件下均能保持超过90％的识别准确率,进一步验证了所提方法的鲁棒性.
Contact-free IR-UWB Human Motion Recognition Based on Dual-stream Fusion Network
With the rapid development of intelligent sensing technology,the field of human computer interaction(HCI)has en-tered 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 tem-poral 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,in-tegrating multi-dimensional convolutional neural networks(CNNs)and GoogLeNet modules,is developed to facilitate precise ac-tion 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.
Human computer interactionWireless sensingImpulse radio ultra-wideband(IR-UWB)Motion recognition
万方数据
维普
