# Using Large Language Models to Compare Explainable Models for Smart Home Human Activity Recognition

arXiv: https://arxiv.org/abs/2408.06352 · HTML: https://mlsanity.com/paper/2408.06352/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 57 references that are in the map.
- First posted on arXiv 24 Jul 2024.

## Builds on (most cited first, 22 of 22)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Deep Learning for Sensor-based Activity Recognition: A Survey](https://mlsanity.com/paper/1707.03502/) (2017)
- [Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities](https://mlsanity.com/paper/2001.07416/) (2020)
- [Assessing the State of Self-Supervised Human Activity Recognition using Wearables](https://mlsanity.com/paper/2202.12938/) (2022)
- [Incorporating Domain Knowledge into Deep Neural Networks](https://mlsanity.com/paper/2103.00180/) (2021)
- [Neuro-Symbolic Artificial Intelligence: Current Trends](https://mlsanity.com/paper/2105.05330/) (2021)
- [HARGPT: Are LLMs Zero-Shot Human Activity Recognizers?](https://mlsanity.com/paper/2403.02727/) (2024)
- [Cross-Subject Transfer Learning in Human Activity Recognition Systems using Generative Adversarial Networks](https://mlsanity.com/paper/1903.12489/) (2019)
- [NumeroLogic: Number Encoding for Enhanced LLMs' Numerical Reasoning](https://mlsanity.com/paper/2404.00459/) (2024)
- [Text Recognition in the Wild: A Survey](https://mlsanity.com/paper/2005.03492/) (2020)
- [TENT: Connect Language Models with IoT Sensors for Zero-Shot Activity Recognition](https://mlsanity.com/paper/2311.08245/) (2023)
- [Knowledge Infused Learning (K-IL): Towards Deep Incorporation of Knowledge in Deep Learning](https://mlsanity.com/paper/1912.00512/) (2019)
- [Generating Virtual On-body Accelerometer Data from Virtual Textual Descriptions for Human Activity Recognition](https://mlsanity.com/paper/2305.03187/) (2023)
- [Explainable Activity Recognition for Smart Home Systems](https://mlsanity.com/paper/2105.09787/) (2021)
- [Transfer Learning in Human Activity Recognition: A Survey](https://mlsanity.com/paper/2401.10185/) (2024)
- [GG-LLM: Geometrically Grounding Large Language Models for Zero-shot Human Activity Forecasting in Human-Aware Task Planning](https://mlsanity.com/paper/2310.20034/) (2023)
- [Unsupervised Human Activity Recognition through Two-stage Prompting with ChatGPT](https://mlsanity.com/paper/2306.02140/) (2023)
- [Context-driven Active and Incremental Activity Recognition](https://mlsanity.com/paper/1906.03033/) (2019)
- [Toward Pioneering Sensors and Features Using Large Language Models in Human Activity Recognition](https://mlsanity.com/paper/2306.16017/) (2023)

## Built on by (most cited first, 9 of 9)

- [UniMTS: Unified Pre-training for Motion Time Series](https://mlsanity.com/paper/2410.19818/) (2024)
- [Past, Present, and Future of Sensor-Based Human Activity Recognition Using Wearables: A Surveying Tutorial on a Still Challenging Task](https://mlsanity.com/paper/2411.14452/) (2024)
- [Large Language Model-Guided Semantic Alignment for Human Activity Recognition](https://mlsanity.com/paper/2410.00003/) (2024)
- [Leveraging Large Language Models for Explainable Activity Recognition in Smart Homes: A Critical Evaluation](https://mlsanity.com/paper/2503.16622/) (2025)
- [A Survey of Foundation Models for IoT: Taxonomy and Criteria-Based Analysis](https://mlsanity.com/paper/2506.12263/) (2025)
- [AgentSense: Virtual Sensor Data Generation Using LLM Agents in Simulated Home Environments](https://mlsanity.com/paper/2506.11773/) (2025)
- [Semantically Encoding Activity Labels for Context-Aware Human Activity Recognition](https://mlsanity.com/paper/2504.07916/) (2025)
- [Rules or Weights? Comparing User Understanding of Explainable AI Techniques with the Cognitive XAI-Adaptive Model](https://mlsanity.com/paper/2602.19620/) (2026)
- [Logi-PAR: Logic-Infused Patient Activity Recognition via Differentiable Rule](https://mlsanity.com/paper/2603.05184/) (2026)

## Abstract (arXiv)

Recognizing daily activities with unobtrusive sensors in smart environments enables various healthcare applications. Monitoring how subjects perform activities at home and their changes over time can reveal early symptoms of health issues, such as cognitive decline. Most approaches in this field use deep learning models, which are often seen as black boxes mapping sensor data to activities. However, non-expert users like clinicians need to trust and understand these models' outputs. Thus, eXplainable AI (XAI) methods for Human Activity Recognition have emerged to provide intuitive natural language explanations from these models. Different XAI methods generate different explanations, and their effectiveness is typically evaluated through user surveys, that are often challenging in terms of costs and fairness. This paper proposes an automatic evaluation method using Large Language Models (LLMs) to identify, in a pool of candidates, the best XAI approach for non-expert users. Our preliminary results suggest that LLM evaluation aligns with user surveys.