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

Skeletal motions have been heavily replied upon for human activity
recognition (HAR). Recently, a universal vulnerability of skeleton-based HAR
has been identified across a variety of classifiers and data, calling for
mitigation. To this end, we propose the first black-box defense method for
skeleton-based HAR to our best knowledge. Our method is featured by full
Bayesian treatments of the clean data, the adversaries and the classifier,
leading to (1) a new Bayesian Energy-based formulation of robust discriminative
classifiers, (2) a new adversary sampling scheme based on natural motion
manifolds, and (3) a new post-train Bayesian strategy for black-box defense. We
name our framework Bayesian Energy-based Adversarial Training or BEAT. BEAT is
straightforward but elegant, which turns vulnerable black-box classifiers into
robust ones without sacrificing accuracy. It demonstrates surprising and
universal effectiveness across a wide range of skeletal HAR classifiers and
datasets, under various attacks. Code is available at
this https URL

这篇文章介绍了一种名为 Bayesian Energy-based Adversarial Training（BEAT）的方法，它可以针对基于骨骼的人体活动识别领域普遍存在的黑盒攻击漏洞进行防御，并能够在不影响分类器准确率的前提下，提高分类器的鲁棒性。