# Explainable Activity Recognition for Smart Home Systems

arXiv: https://arxiv.org/abs/2105.09787 · HTML: https://mlsanity.com/paper/2105.09787/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 89 references that are in the map.
- First posted on arXiv 20 May 2021.

## Builds on (most cited first, 19 of 19)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [A Survey on Explainable Artificial Intelligence (XAI): Towards Medical XAI](https://mlsanity.com/paper/1907.07374/) (2019)
- [Metrics for Explainable AI: Challenges and Prospects](https://mlsanity.com/paper/1812.04608/) (2018)
- [Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Survey](https://mlsanity.com/paper/2006.11371/) (2020)
- [Visual Interpretability for Deep Learning: a Survey](https://mlsanity.com/paper/1802.00614/) (2018)
- [Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model](https://mlsanity.com/paper/1511.01644/) (2015)
- [Human-centered Explainable AI: Towards a Reflective Sociotechnical Approach](https://mlsanity.com/paper/2002.01092/) (2020)
- [Interpreting CNNs via Decision Trees](https://mlsanity.com/paper/1802.00121/) (2018)
- [Automated Rationale Generation: A Technique for Explainable AI and its Effects on Human Perceptions](https://mlsanity.com/paper/1901.03729/) (2019)
- [Beyond Sparsity: Tree Regularization of Deep Models for Interpretability](https://mlsanity.com/paper/1711.06178/) (2017)
- [Faithful Multimodal Explanation for Visual Question Answering](https://mlsanity.com/paper/1809.02805/) (2018)
- [Explainable AI for Robot Failures: Generating Explanations that Improve User Assistance in Fault Recovery](https://mlsanity.com/paper/2101.01625/) (2021)
- [Generating Token-Level Explanations for Natural Language Inference](https://mlsanity.com/paper/1904.10717/) (2019)
- [Understanding and Improving Recurrent Networks for Human Activity Recognition by Continuous Attention](https://mlsanity.com/paper/1810.04038/) (2018)
- [Don't Explain without Verifying Veracity: An Evaluation of Explainable AI with Video Activity Recognition](https://mlsanity.com/paper/2005.02335/) (2020)

## Built on by (most cited first, 14 of 14)

- [Layout Agnostic Human Activity Recognition in Smart Homes through Textual Descriptions Of Sensor Triggers (TDOST)](https://mlsanity.com/paper/2405.12368/) (2024)
- [Using Large Language Models to Compare Explainable Models for Smart Home Human Activity Recognition](https://mlsanity.com/paper/2408.06352/) (2024)
- [Explainable Artificial Intelligence (XAI) for Internet of Things: A Survey](https://mlsanity.com/paper/2206.04800/) (2022)
- [Towards LLM-Powered Ambient Sensor Based Multi-Person Human Activity Recognition](https://mlsanity.com/paper/2407.09529/) (2024)
- [Leveraging Large Language Models for Explainable Activity Recognition in Smart Homes: A Critical Evaluation](https://mlsanity.com/paper/2503.16622/) (2025)
- [ContextDet: Temporal Action Detection with Adaptive Context Aggregation](https://mlsanity.com/paper/2410.15279/) (2024)
- [SEZ-HARN: Self-Explainable Zero-shot Human Activity Recognition Network](https://mlsanity.com/paper/2507.00050/) (2025)
- [Privacy-Preserving Explainable AIoT Application via SHAP Entropy Regularization](https://mlsanity.com/paper/2511.09775/) (2025)
- [ContextGPT: Infusing LLMs Knowledge into Neuro-Symbolic Activity Recognition Models](https://mlsanity.com/paper/2403.06586/) (2024)
- [GNN-XAR: A Graph Neural Network for Explainable Activity Recognition in Smart Homes](https://mlsanity.com/paper/2502.17999/) (2025)
- [Who Benefits from AI Explanations? Towards Accessible and Interpretable Systems](https://mlsanity.com/paper/2508.10806/) (2025)
- [From Facts to Foils: Designing and Evaluating Counterfactual Explanations for Smart Environments](https://mlsanity.com/paper/2510.03078/) (2025)
- [Explainable Human Activity Recognition: A Unified Review of Concepts and Mechanisms](https://mlsanity.com/paper/2604.09799/) (2026)
- [LastAct: Trajectory-Guided Latest-Activity Localization for Real-Time Smart-Home Activity Recognition](https://mlsanity.com/paper/2606.00260/) (2026)

## Abstract (arXiv)

Smart home environments are designed to provide services that help improve the quality of life for the occupant via a variety of sensors and actuators installed throughout the space. Many automated actions taken by a smart home are governed by the output of an underlying activity recognition system. However, activity recognition systems may not be perfectly accurate and therefore inconsistencies in smart home operations can lead users reliant on smart home predictions to wonder "why did the smart home do that?" In this work, we build on insights from Explainable Artificial Intelligence (XAI) techniques and introduce an explainable activity recognition framework in which we leverage leading XAI methods to generate natural language explanations that explain what about an activity led to the given classification. Within the context of remote caregiver monitoring, we perform a two-step evaluation: (a) utilize ML experts to assess the sensibility of explanations, and (b) recruit non-experts in two user remote caregiver monitoring scenarios, synchronous and asynchronous, to assess the effectiveness of explanations generated via our framework. Our results show that the XAI approach, SHAP, has a 92% success rate in generating sensible explanations. Moreover, in 83% of sampled scenarios users preferred natural language explanations over a simple activity label, underscoring the need for explainable activity recognition systems. Finally, we show that explanations generated by some XAI methods can lead users to lose confidence in the accuracy of the underlying activity recognition model. We make a recommendation regarding which existing XAI method leads to the best performance in the domain of smart home automation, and discuss a range of topics for future work to further improve explainable activity recognition.