<ul><li>Explaining machine learning (ML) models using eXplainable AI (XAI) techniques has become essential to make them more transparent and trustworthy.</li><li>A comparative analysis of Shapley Additive Explanations (SHAP) and Gradient-weighted Class Activation Mapping (Grad-CAM) methods in human activity recognition (HAR) is presented.</li><li>The study evaluates these methods on real-world datasets, providing insights into their strengths, limitations, and differences.</li><li>SHAP and Grad-CAM can complement each other to provide more interpretable and actionable model explanations, enhancing trust and transparency in ML models.</li></ul>

Choose Your Explanation: A Comparison of SHAP and GradCAM in Human Activity Recognition

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