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作 者:Feng Yufei Zhong Xiaofeng Chen Xinwei Zhou Shidong
机构地区:[1]Department of Electronic Engineering,Tsinghua University,Beijing 100084,China [2]State Key Laboratory of Space Network and Communications(Tsinghua University),Beijing 100084,China [3]Beijing National Research Center for Information Science and Technology,Beijing 100084,China
出 处:《China Communications》2025年第4期174-201,共28页中国通信(英文版)
基 金:supported by National 2011 Collaborative Innovation Center of Wireless Communication Technologies under Grant 2242022k60006。
摘 要:This paper presents a comprehensive framework that enables communication scene recognition through deep learning and multi-sensor fusion.This study aims to address the challenge of current communication scene recognition methods that struggle to adapt in dynamic environments,as they typically rely on post-response mechanisms that fail to detect scene changes before users experience latency.The proposed framework leverages data from multiple smartphone sensors,including acceleration sensors,gyroscopes,magnetic field sensors,and orientation sensors,to identify different communication scenes,such as walking,running,cycling,and various modes of transportation.Extensive experimental comparative analysis with existing methods on the open-source SHL-2018 dataset confirmed the superior performance of our approach in terms of F1 score and processing speed.Additionally,tests using a Microsoft Surface Pro tablet and a self-collected Beijing-2023 dataset have validated the framework's efficiency and generalization capability.The results show that our framework achieved an F1 score of 95.15%on SHL-2018and 94.6%on Beijing-2023,highlighting its robustness across different datasets and conditions.Furthermore,the levels of computational complexity and power consumption associated with the algorithm are moderate,making it suitable for deployment on mobile devices.
关 键 词:communication scene recognition deep learning sensor fusion SHL smartphone-based applications
分 类 号:TP391.4[自动化与计算机技术—计算机应用技术]
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