Scholarlink

<p>WiFi-based human activity recognition (HAR) leveraging channel state information (CSI) faces challenges due to multipath effects, leading to ambiguous feature representation. To address this, we propose AMCS-TCN, a model integrating dual attention mechanisms and a multi-channel, multi-scale temporal convolutional network (TCN). This model utilizes spatial diversity from MIMO systems to enhance feature representation. The dual attention module, comprising feature map fusion attention (FMFA) and Feature Channel Attention (FCA), adaptively focuses on discriminative information. Experimental results on a public dataset and real-world environments show an average accuracy of 98.85% and robust performance in complex settings. Our code and models are released at <a href="https://github.com/boyuanzhang759-rgb/AMCS-TCN-by-BoYuan">https://github.com/boyuanzhang759-rgb/AMCS-TCN-by-BoYuan</a> for reproduction.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Multi-channel TCN with dual attention for robust human activity recognition

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月24日

查看全文
  • Boyuan Zhang,
  • Hongkai Zeng,
  • Tianhong Lv,
  • Lijun Xu

摘要

WiFi-based human activity recognition (HAR) leveraging channel state information (CSI) faces challenges due to multipath effects, leading to ambiguous feature representation. To address this, we propose AMCS-TCN, a model integrating dual attention mechanisms and a multi-channel, multi-scale temporal convolutional network (TCN). This model utilizes spatial diversity from MIMO systems to enhance feature representation. The dual attention module, comprising feature map fusion attention (FMFA) and Feature Channel Attention (FCA), adaptively focuses on discriminative information. Experimental results on a public dataset and real-world environments show an average accuracy of 98.85% and robust performance in complex settings. Our code and models are released at https://github.com/boyuanzhang759-rgb/AMCS-TCN-by-BoYuan for reproduction.