From Movements to Metrics: Evaluating Explainable AI Methods in Skeleton-Based Human Activity Recognition
Kimji N. Pellano,
Inga Strümke,
Espen A. F. Ihlen
Abstract: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 Mappin… Show more
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