基于表征知识蒸馏的WiFi手势识别方法  

A representation knowledge distillation-based WiFi gesture recognition method

作　　者：龚浩成 朱海 黄子非 杨明泽 张开昱 吴飞[1] GONG Haocheng;ZHU Hai;HUANG Zifei;YANG Mingze;ZHANG Kaiyu;WU Fei(School of Electronic and Electrical Engineering,Shanghai University of Engineering Science,Shanghai 201620,China)

机构地区：[1]上海工程技术大学电子电气工程学院,上海201620

出　　处：《计算机工程与科学》2025年第4期655-666,共12页Computer Engineering & Science

基　　金：国家自然科学基金(61902237)。

摘　　要：随着人工智能和无线传感技术的快速发展,WiFi手势识别已经成为备受关注的研究领域之一。当前的研究工作,为了提高在不同数据域中模型的鲁棒性,减少对模型重新训练的依赖,通过从信道状态信息CSI中提取域无关特征,提出了身体坐标速度谱BVP,可实现在域内和跨域识别上的高准确性。然而在实际场景中,将采集到的CSI信号转换为BVP需要耗费大量计算资源,无法满足在生产环境中所需的实时性和扩展性等需求。此外,使用传统模型处理大量复杂的数据时,其缺乏全局特征和长期依赖关系的捕捉能力。为了解决上述问题,提出了一种基于表征知识蒸馏的WiFi手势识别框架RKD-WGR。RKD-WGR首先利用BVP数据作为教师模型输入,指导利用CSI数据输入的学生模型,将BVP推理分辨能力整合到学生模型中,也让CSI从自身学习来弥补BVP缺失的信息。同时,为了提高识别性能并加强教师模型向学生模型的知识传授能力,提出了3DWiT作为教师模型,利用BVP的时空信息辅助教师模型获取更多的信息来增强知识传授能力。实验结果表明,在Widar 3.0数据集上,不使用BVP而仅使用CSI的情况下,6类手势识别的精确度达到了97.1%,10类手势识别的精确度为96.5%,而22类手势识别的精确度达到了89.5%,验证了所提出框架和模型的有效性。With the rapid development of artificial intelligence and wireless sensing technologies,WiFi gesture recognition has emerged as one of the research areas attracting significant attention.Current research efforts aim to enhance the robustness of models across different data domains and reduce the reliance on retraining by extracting domain-independent features from channel state information(CSI)and proposing the body coordinate velocity profile(BVP).This enables high accuracy in both intra-domain and cross-domain recognition.However,in practical scenarios,converting collected CSI signals into BVP requires substantial computational resources,falling short of meeting the real-time and scalability requirements in production environments.Additionally,traditional models lack the capability to capture global features and long-term dependencies when dealing with large and complex datasets.To address these issues,a representation knowledge distillation-based WiFi gesture recognition(RKD-WGR)framework is proposed.RKD-WGR utilizes BVP data as input for the teacher model to guide the student model,which uses CSI data as input.This integrates the BVP inference capability into the student model while allowing CSI to learn from itself to complement information missing from BVP.Meanwhile,to improve recognition performance and strengthen the knowledge transfer from the teacher model to the student model,a 3D WiFi Transformer(3DWiT)is introduced as the teacher model.It leverages the spatio-temporal information of BVP to assist the teacher model in acquiring more information and enhancing its knowledge transfer capability.Experimental results on Widar 3.0 dataset demonstrate that,without using BVP and solely relying on CSI,the accuracy for six gesture classes reach 97.1%,for ten gesture classes it is 96.5%,and for 22 gesture classes it achieves 89.5%.These results validate the effectiveness of the proposed framework and model.

关 键 词：WIFI 信道状态信息 手势识别 知识蒸馏 Vision Transformer 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]