无需训练数据，自动识别新动作并生成解释视频。
SEZ-HARN: Self-Explainable Zero-shot Human Activity Recognition Network
- 基于自解释机制，零样本识别未见动作。
- 在PAMAP2上准确率仅比顶尖模型低3%。
- 生成骨架视频解释决策过程，适合医疗场景使用。
人体活动识别（HAR）利用惯性测量单元（IMU）传感器数据，在医疗和辅助生活领域有广泛应用。然而，真实场景应用受限于缺乏覆盖广泛动作的完整IMU-HAR数据集，以及现有模型缺乏透明性。零样本HAR（ZS-HAR）可缓解数据不足问题，但当前模型难以解释其决策过程。本文提出一种新型基于IMU的零样本HAR模型——自解释零样本人体活动识别网络（SEZ-HARN），可在未见过的活动中进行识别，并生成骨架视频以解释其决策逻辑。我们在四个基准数据集PAMAP2、DaLiAc、HTD-MHAD和MHealth上评估了SEZ-HARN，与三种前沿黑箱ZS-HAR模型对比。实验表明，SEZ-HARN在保持与其他三数据集相当性能的同时，零样本预测准确率在PAMAP2上仅比最优黑箱模型低3%，且生成的解释具有真实性和可读性。
原文摘要 · Abstract (English)
Human Activity Recognition (HAR), which uses data from Inertial Measurement Unit (IMU) sensors, has many practical applications in healthcare and assisted living environments. However, its use in real-world scenarios has been limited by the lack of comprehensive IMU-based HAR datasets that cover a wide range of activities and the lack of transparency in existing HAR models. Zero-shot HAR (ZS-HAR) overcomes the data limitations, but current models struggle to explain their decisions, making them less transparent. This paper introduces a novel IMU-based ZS-HAR model called the Self-Explainable Zero-shot Human Activity Recognition Network (SEZ-HARN). It can recognize activities not encountered during training and provide skeleton videos to explain its decision-making process. We evaluate the effectiveness of the proposed SEZ-HARN on four benchmark datasets PAMAP2, DaLiAc, HTD-MHAD and MHealth and compare its performance against three state-of-the-art black-box ZS-HAR models. The experiment results demonstrate that SEZ-HARN produces realistic and understandable explanations while achieving competitive Zero-shot recognition accuracy. SEZ-HARN achieves a Zero-shot prediction accuracy within 3\% of the best-performing black-box model on PAMAP2 while maintaining comparable performance on the other three datasets.
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