  Integrating inertial measurement units (IMUs) with large language models
(LLMs) advances multimodal AI by enhancing human activity understanding. We
introduce SensorCaps, a dataset of 26,288 IMU-derived activity narrations, and
OpenSQA, an instruction-following dataset with 257,562 question-answer pairs.
Combining LIMU-BERT and Llama, we develop LLaSA, a Large Multimodal Agent
capable of interpreting and responding to activity and motion analysis queries.
Our evaluation demonstrates LLaSA's effectiveness in activity classification
and question answering, highlighting its potential in healthcare, sports
science, and human-computer interaction. These contributions advance
sensor-aware language models and open new research avenues. Our code repository
and datasets can be found on https://github.com/BASHLab/LLaSA.
