Human activity recognition (HAR) is an active research area that is currently being applied to various healthcare applications such as fall detection, assisted living, etc. These applications make use of the Internet of Things which is widespread across today's world. One of the major challenges in these applications is the need for quick intelligent decisions. The deployment of hierarchical edge-fog-cloud computing architecture is the potential solution to address the latency issue. In this article, we proposed an interpretable human activity recognition (IHAR) framework that supports HAR, leveraging the advantages of the fog layer. The proposed framework used a deep learning (DL) model at the cloud infrastructure to classify the activities of humans. The trained DL model is made to run on local fog nodes. However, the DL model being black-box does not provide any explanations for the output to make them acceptable by the users and physicians. Hence, this article also incorporated an explainable artificial intelligence model at the fog layer, to gain insights into the classified output of the DL model. The effective model-level insights emphasized the need for explainable HAR. The article also included an analysis of the results of the existing explainable AI models such as LIME and SHAP to understand which model leads to better performance in the domain of HAR. Results show that the SHAP model has a 33% higher success rate in generating explanations when compared to the LIME model.

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