计算机科学
活动识别
传感器融合
特征(语言学)
模式识别(心理学)
分类
融合
特征提取
接头(建筑物)
人工智能
工程类
哲学
语言学
建筑工程
作者
Y. Zhang,Gongpu Wang,Heng Liu,Wei Gong,Feifei Gao
出处
期刊:IEEE Internet of Things Journal
[Institute of Electrical and Electronics Engineers]
日期:2024-05-07
卷期号:11 (18): 29335-29347
被引量:3
标识
DOI:10.1109/jiot.2024.3397708
摘要
Utilizing communication signals for indoor human activity recognition (HAS) is an important component of integrated sensing and communication (ISAC). The current majority HAS solutions adopt a single sensing strategy and only work in a simple environment. In this paper, we propose a new HAS method named WiSMLF that can flexibly select multiple sensing strategies and then use multi-level feature fusion for sensing. We first use the high frequency energy (HFE) method to categorize human activities into two types: static activities (SAs) and moving activities (MAs). Subsequently, for SAs, we adopt a joint localization and activity recognition sensing strategy, and use a multi-level feature fusion network based on visual geometry group (VGG). For MAs, we adopt a joint activity recognition and moving distance estimation sensing strategy, and use a multi-level feature fusion network based on long short-term memory (LSTM). The experimental results show that WiSMLF outperforms the existing methods especially in complex environments, and can obtain 92% higher accuracy in location, activity recognition, and distance estimation.
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