Explainable Human Activity Recognition: A Unified Review of Concepts and Mechanisms
arXiv cs.LG / 4/14/2026
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Key Points
- The paper reviews explainable human activity recognition (XAI-HAR) approaches aimed at making deep learning-based HAR models more transparent and trustworthy for real-world deployment.
- It proposes a unified framework that distinguishes conceptual dimensions of explainability from the specific algorithmic explanation mechanisms used in different HAR settings.
- The authors present a mechanism-centric taxonomy spanning wearable, ambient, physiological, and multimodal sensing scenarios, accounting for HAR’s temporal, multimodal, and semantic complexities.
- The review summarizes interpretability goals, explanation targets, limitations, and how current evaluation practices measure XAI-HAR reliability.
- It identifies key challenges for achieving dependable, deployable XAI-HAR and outlines research directions toward more human-centered activity recognition systems.
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