  Mobile sensing plays a crucial role in generating digital traces to
understand human daily lives. However, studying behaviours like mood or sleep
quality in smartphone users requires carefully designed mobile sensing
strategies such as sensor selection and feature construction. This process is
time-consuming, burdensome, and requires expertise in multiple domains.
Furthermore, the resulting sensing framework lacks generalizability, making it
difficult to apply to different scenarios. To address these challenges, we
propose an automated mobile sensing strategy for human behaviour understanding.
First, we establish a knowledge base and consolidate rules for effective
feature construction, data collection, and model selection. Then, we introduce
the multi-granular human behaviour representation and design procedures for
leveraging large language models to generate strategies. Our approach is
validated through blind comparative studies and usability evaluation.
Ultimately, our approach holds the potential to revolutionise the field of
mobile sensing and its applications.
