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    <abstract>We introduce SensorLLM, a two-stage framework that enables Large Language Models (LLMs) to perform human activity recognition (HAR) from sensor time-series data. Despite their strong reasoning and generalization capabilities, LLMs remain underutilized for motion sensor data due to the lack of semantic context in time-series, computational constraints, and challenges in processing numerical inputs. SensorLLM addresses these limitations through a Sensor-Language Alignment stage, where the model aligns sensor inputs with trend descriptions. Special tokens are introduced to mark channel boundaries. This alignment enables LLMs to capture numerical variations, channel-specific features, and data of varying durations, without requiring human annotations. In the subsequent Task-Aware Tuning stage, we refine the model for HAR classification, achieving performance that matches or surpasses state-of-the-art methods. Our results demonstrate that SensorLLM evolves into an effective sensor learner, reasoner, and classifier through human-intuitive Sensor-Language Alignment, generalizing across diverse HAR datasets. We believe this work establishes a foundation for future research on time-series and text alignment, paving the way for foundation models in sensor data analysis. Our codes are available at https://github.com/zechenli03/SensorLLM.</abstract>
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%0 Conference Proceedings
%T SensorLLM: Aligning Large Language Models with Motion Sensors for Human Activity Recognition
%A Li, Zechen
%A Deldari, Shohreh
%A Chen, Linyao
%A Xue, Hao
%A Salim, Flora D.
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F li-etal-2025-sensorllm
%X We introduce SensorLLM, a two-stage framework that enables Large Language Models (LLMs) to perform human activity recognition (HAR) from sensor time-series data. Despite their strong reasoning and generalization capabilities, LLMs remain underutilized for motion sensor data due to the lack of semantic context in time-series, computational constraints, and challenges in processing numerical inputs. SensorLLM addresses these limitations through a Sensor-Language Alignment stage, where the model aligns sensor inputs with trend descriptions. Special tokens are introduced to mark channel boundaries. This alignment enables LLMs to capture numerical variations, channel-specific features, and data of varying durations, without requiring human annotations. In the subsequent Task-Aware Tuning stage, we refine the model for HAR classification, achieving performance that matches or surpasses state-of-the-art methods. Our results demonstrate that SensorLLM evolves into an effective sensor learner, reasoner, and classifier through human-intuitive Sensor-Language Alignment, generalizing across diverse HAR datasets. We believe this work establishes a foundation for future research on time-series and text alignment, paving the way for foundation models in sensor data analysis. Our codes are available at https://github.com/zechenli03/SensorLLM.
%U https://aclanthology.org/2025.emnlp-main.19/
%P 354-379

Markdown (Informal)

[SensorLLM: Aligning Large Language Models with Motion Sensors for Human Activity Recognition](https://aclanthology.org/2025.emnlp-main.19/) (Li et al., EMNLP 2025)

• SensorLLM: Aligning Large Language Models with Motion Sensors for Human Activity Recognition (Li et al., EMNLP 2025)

ACL

• Zechen Li, Shohreh Deldari, Linyao Chen, Hao Xue, and Flora D. Salim. 2025. SensorLLM: Aligning Large Language Models with Motion Sensors for Human Activity Recognition. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 354–379, Suzhou, China. Association for Computational Linguistics.