Leveraging Large Language Models for Explainable Activity Recognition in Smart Homes: A Critical Evaluation

• URL: http://arxiv.org/abs/2503.16622v1
• Date: Thu, 20 Mar 2025 18:23:03 GMT
• Title: Leveraging Large Language Models for Explainable Activity Recognition in Smart Homes: A Critical Evaluation
• Authors: Michele Fiori, Gabriele Civitarese, Priyankar Choudhary, Claudio Bettini,
• Abstract summary: XAI has been applied to sensor-based Activities of Daily Living (ADLs) recognition in smart homes.<n>This paper investigates potential approaches to combine XAI and Large Language Models (LLMs) for sensor-based ADL recognition.
• Score: 0.29998889086656577
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Explainable Artificial Intelligence (XAI) aims to uncover the inner reasoning of machine learning models. In IoT systems, XAI improves the transparency of models processing sensor data from multiple heterogeneous devices, ensuring end-users understand and trust their outputs. Among the many applications, XAI has also been applied to sensor-based Activities of Daily Living (ADLs) recognition in smart homes. Existing approaches highlight which sensor events are most important for each predicted activity, using simple rules to convert these events into natural language explanations for non-expert users. However, these methods produce rigid explanations lacking natural language flexibility and are not scalable. With the recent rise of Large Language Models (LLMs), it is worth exploring whether they can enhance explanation generation, considering their proven knowledge of human activities. This paper investigates potential approaches to combine XAI and LLMs for sensor-based ADL recognition. We evaluate if LLMs can be used: a) as explainable zero-shot ADL recognition models, avoiding costly labeled data collection, and b) to automate the generation of explanations for existing data-driven XAI approaches when training data is available and the goal is higher recognition rates. Our critical evaluation provides insights into the benefits and challenges of using LLMs for explainable ADL recognition.

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