利用Wi-Fi相位信息提升机械臂动作识别准确率
Beyond Amplitude: Channel State Information Phase-Aware Deep Fusion for Robotic Activity Recognition
- 分离编码幅度与相位，通过学习门控机制融合特征
- 结合相位使识别准确率提升，跨速度场景更稳定
- 首个系统性探索相位在机器人动作识别中作用的工作
Wi-Fi信道状态信息（CSI）已成为非视距人体与机器人动作识别的有前景感知模态。然而，以往研究主要依赖于CSI幅度，而忽视了相位信息，特别是在机械臂动作识别中。本文提出门控融合双向长短期记忆网络（GF-BiLSTM），用于机器人活动识别中的Wi-Fi感知。该模型为双流门控融合网络，分别编码幅度与相位，并通过学习到的门控机制自适应地整合每时刻特征。我们在四种输入配置下，采用留一速度法（LOVO）系统评估了当前最先进的深度学习模型：仅幅度、仅相位、幅度+未解缠相位、幅度+清洗后相位。实验结果表明，将相位与幅度结合能持续提升识别准确率和跨速度鲁棒性，且GF-BiLSTM表现最佳。据我们所知，本工作首次系统性探索了CSI相位在机器人活动识别中的应用，确立了其在基于Wi-Fi感知中的关键作用。
原文摘要 · Abstract (English)
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.
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