The advancement of deep learning in human activity recognition (HAR) using 3D
skeleton data is critical for applications in healthcare, security, sports, and
human-computer interaction. This paper tackles a well-known gap in the field,
which is the lack of testing in the applicability and reliability of XAI
evaluation metrics in the skeleton-based HAR domain. We have tested established
XAI metrics namely faithfulness and stability on Class Activation Mapping (CAM)
and Gradient-weighted Class Activation Mapping (Grad-CAM) to address this
problem. The study also introduces a perturbation method that respects human
biomechanical constraints to ensure realistic variations in human movement. Our
findings indicate that \textit{faithfulness} may not be a reliable metric in
certain contexts, such as with the EfficientGCN model. Conversely, stability
emerges as a more dependable metric when there is slight input data
perturbations. CAM and Grad-CAM are also found to produce almost identical
explanations, leading to very similar XAI metric performance. This calls for
the need for more diversified metrics and new XAI methods applied in
skeleton-based HAR.

本研究探讨了骨架型人类活动识别领域中的 XAI 评估指标的适用性和可靠性，并通过测试 faithfulness 和 stability 等已建立的 XAI 评估指标来解决了该领域中的一个问题。研究还引入了一种尊重人体生物力学约束的扰动方法，以确保人体运动的真实变化，发现在一些情境下 faithfulness 并不可靠，而 stability 则表现更加可信。此外，CAM 和 Grad-CAM 产生的解释几乎完全相同，导致 XAI 评估指标的性能非常相似，这需要更多多样化的指标和新的 XAI 方法应用于骨架型人类活动识别。

从动作到指标：评估基于骨架的人类活动识别中的可解释 AI 方法

From Movements to Metrics: Evaluating Explainable AI Methods in  Skeleton-Based Human Activity Recognition

We argue that, when learning a 1-Lipschitz neural network with the dual loss
of an optimal transportation problem, the gradient of the model is both the
direction of the transportation plan and the direction to the closest
adversarial attack. Traveling along the gradient to the decision boundary is no
more an adversarial attack but becomes a counterfactual explanation, explicitly
transporting from one class to the other. Through extensive experiments on XAI
metrics, we find that the simple saliency map method, applied on such networks,
becomes a reliable explanation, and outperforms the state-of-the-art
explanation approaches on unconstrained models. The proposed networks were
already known to be certifiably robust, and we prove that they are also
explainable with a fast and simple method.

本研究采用 1-Lipschitz 神经网络学习最佳传输问题的双重损失，研究发现，模型的梯度既是传输计划的方向，也是最接近的对抗攻击方向。通过在此类网络上应用简单的显著性图方法，我们得出结论：这种方法成为了可靠的解释方法，并在无限制模型上表现优于现有技术。我们提出的网络已被证明具有认证的鲁棒性，并证明了它们也可以用一种快速简单的方法进行解释。