SensorLLM: Aligning Large Language Models with Motion Sensors for Human Activity Recognition
Keywords: Large language models, Multi-modal, alignment, human activity recognition, sensor, time-series
TL;DR: We present SensorLLM, a two-stage framework that aligns sensor data with automatically generated text to enable LLMs to analyse sensor data and perform human activity recognition with state-of-the-art accuracy.
Abstract: In this work, we bridge the gap between wearable sensor technology and personalized AI assistants by enabling Large Language Models (LLMs) to understand time-series tasks like human activity recognition (HAR). Despite the strong reasoning and generalization capabilities of LLMs, leveraging them for sensor data tasks remains largely unexplored. This gap stems from challenges like the lack of semantic context in time-series data, computational limitations, and LLMs' difficulty processing numerical inputs. To address these issues, we introduce SensorLLM, a two-stage framework to unlock LLMs’ potential for sensor data tasks. In the Sensor-Language Alignment Stage, we introduce special tokens for each sensor channel and automatically generate trend-descriptive text to align sensor data with textual inputs, enabling SensorLLM to capture numerical changes, channel-specific information, and sensor data of varying lengths—capabilities that existing LLMs typically struggle with, all without the need for human annotations. Next, in Task-Aware Tuning Stage, we refine the model for HAR classification using the frozen LLM and alignment module, achieving performance on par with or surpassing state-of-the-art models. We further demonstrate that SensorLLM evolves into an effective sensor learner, reasoner, and classifier through Sensor-Language Alignment, enabling it to generalize across diverse datasets for HAR tasks. We strongly believe our work lays the stepstone for future time-series and text alignment research, offering a path toward foundation models for sensor data. Our codes are available at https://anonymous.4open.science/r/sensorllm_code-E0FC.
Primary Area: applications to computer vision, audio, language, and other modalities
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Submission Number: 4049
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