From Movements to Metrics: Evaluating Explainable AI Methods in
  Skeleton-Based Human Activity Recognition
Kimji N. Pellano Inga Strümke Espen Alexander F. Ihlen
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.