Abstract
Wi-Fi Channel State Information (CSI) has emerged as a promising non-line-of-sight sensing modality for human and robotic activity recognition. However, prior work has predominantly relied on CSI amplitude while underutilizing phase information, particularly in robotic arm activity recognition. In this paper, we present GateFusion-Bidirectional Long Short-Term Memory network (GF-BiLSTM) for WiFi sensing in robotic activity recognition. GF-BiLSTM is a two-stream gated fusion network that encodes amplitude and phase separately and adaptively integrates per-time features through a learned gating mechanism. We systematically evaluate state-of-the-art deep learning models under a Leave-One-Velocity-Out (LOVO) protocol across four input configurations: amplitude only, phase only, amplitude + unwrapped phase, and amplitude + sanitized phase. Experimental results demonstrate that incorporating phase alongside amplitude consistently improves recognition accuracy and cross-speed robustness, with GF-BiLSTM achieving the best performance. To the best of our knowledge, this work provides the first systematic exploration of CSI phase for robotic activity recognition, establishing its critical role in Wi-Fi-based sensing.
Abstract (translated)
Wi-Fi 信道状态信息（CSI）已成为非直视线感知模式中用于人类和机器人活动识别的有前途的技术。然而，先前的工作主要依赖于CSI幅度信息，而忽视了相位信息的应用，尤其是在机器人手臂活动识别方面。本文提出了一种基于双流门控融合网络的GateFusion-Bidirectional Long Short-Term Memory (GF-BiLSTM)模型，用于通过Wi-Fi传感进行机器人活动识别。GF-BiLSTM将幅度和相位分别编码，并通过学习到的门机制自适应地集成每个时间点上的特征。我们在四种输入配置下对最先进的深度学习模型进行了系统的评估：仅幅度、仅相位、幅度+未展开相位，以及幅度+净化后的相位。实验结果表明，在所有情况下，结合使用幅度和相位信息都能持续提高识别准确性和跨速度的鲁棒性，其中GF-BiLSTM的表现最佳。据我们所知，这项工作首次系统地探索了CSI相位在机器人活动识别中的作用，并确立了它在基于Wi-Fi感知中的关键角色。
URL
https://arxiv.org/abs/2603.09047
