Sensor data streams provide valuable information around activities and context for downstream applications, though integrating complementary information can be challenging. We show that large language models (LLMs) can be used for late fusion for activity classification from audio and motion time series data. We curated a subset of data for diverse activity recognition across contexts (e.g., household activities, sports) from the Ego4D dataset. Evaluated LLMs achieved 12-class zero- and one-shot classification F1-scores significantly above chance, with no task-specific training. Zero-shot classification via LLM-based fusion from modality-specific models can enable multimodal temporal applications where there is limited aligned training data for learning a shared embedding space. Additionally, LLM-based fusion can enable model deploying without requiring additional memory and computation for targeted application-specific multimodal models.
@article{arxiv.2509.10729,
title = {Using LLMs for Late Multimodal Sensor Fusion for Activity Recognition},
author = {Ilker Demirel and Karan Thakkar and Benjamin Elizalde and Miquel Espi Marques and Aditya Sarathy and Yang Bai and Umamahesh Srinivas and Jiajie Xu and Shirley Ren and Jaya Narain},
journal= {arXiv preprint arXiv:2509.10729},
year = {2025}
}
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NeurIPS Workshop on Learning from Time Series for Health