Improving Zero-shot ADL Recognition with Large Language Models through Event-based Context and Confidence

• URL: http://arxiv.org/abs/2601.08241v1
• Date: Tue, 13 Jan 2026 05:58:24 GMT
• Title: Improving Zero-shot ADL Recognition with Large Language Models through Event-based Context and Confidence
• Authors: Michele Fiori, Gabriele Civitarese, Marco Colussi, Claudio Bettini,
• Abstract summary: Sensor-based recognition of Activities of Daily Living in smart homes supports applications such as healthcare, safety, and energy management.<n>Recent zero-shot methods based on Large Language Models (LLMs) have the advantage of removing the reliance on labeled ADL sensor data.<n>This paper proposes to improve zero-shot ADL recognition with event-based segmentation and a novel method for estimating prediction confidence.
• Score: 1.2599533416395765
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Unobtrusive sensor-based recognition of Activities of Daily Living (ADLs) in smart homes by processing data collected from IoT sensing devices supports applications such as healthcare, safety, and energy management. Recent zero-shot methods based on Large Language Models (LLMs) have the advantage of removing the reliance on labeled ADL sensor data. However, existing approaches rely on time-based segmentation, which is poorly aligned with the contextual reasoning capabilities of LLMs. Moreover, existing approaches lack methods for estimating prediction confidence. This paper proposes to improve zero-shot ADL recognition with event-based segmentation and a novel method for estimating prediction confidence. Our experimental evaluation shows that event-based segmentation consistently outperforms time-based LLM approaches on complex, realistic datasets and surpasses supervised data-driven methods, even with relatively small LLMs (e.g., Gemma 3 27B). The proposed confidence measure effectively distinguishes correct from incorrect predictions.

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