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Walking direction recognition is vital for HCI, healthcare monitoring, and navigation, but real-world data are highly imbalanced: non-straight trajectories far outnumber straight ones, biasing classifiers and reducing accuracy. We collect synchronized inertial and plantar-pressure signals from wearables and deliberately construct an imbalanced dataset to mirror practice. We then propose a multimodal framework that couples CNN-based spatiotemporal feature extractors with a DRL Q-network; a reward-driven optimization promotes balanced decisions under skewed label distributions. Experiments on the constructed dataset show consistent gains over state-of-the-art methods, with the largest improvements under severe imbalance, demonstrating robustness and suitability for deployment in wearable gait analysis systems.

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A CNN-Based Deep Reinforcement Learning Approach for Imbalanced Walking Direction Recognition

  • 会议论文

  • 发表日期：

    2026年

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  • Chenchen Yang,
  • Hankun Xu,
  • Ming Guo,
  • Xiangyong Chen,
  • Jianlong Qiu,
  • Shouyi Li

摘要

Walking direction recognition is vital for HCI, healthcare monitoring, and navigation, but real-world data are highly imbalanced: non-straight trajectories far outnumber straight ones, biasing classifiers and reducing accuracy. We collect synchronized inertial and plantar-pressure signals from wearables and deliberately construct an imbalanced dataset to mirror practice. We then propose a multimodal framework that couples CNN-based spatiotemporal feature extractors with a DRL Q-network; a reward-driven optimization promotes balanced decisions under skewed label distributions. Experiments on the constructed dataset show consistent gains over state-of-the-art methods, with the largest improvements under severe imbalance, demonstrating robustness and suitability for deployment in wearable gait analysis systems.