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.

翻译：移动感知在生成数字痕迹以理解人类日常生活方面发挥着关键作用。然而，研究智能手机用户的情绪或睡眠质量等行为需要精心设计的移动感知策略，例如传感器选择与特征构建。这一过程耗时费力，且需要多领域专业知识。此外，生成的感知框架缺乏泛化能力，难以应用于不同场景。为解决这些挑战，我们提出了一种用于人类行为理解的自动化移动感知策略。首先，我们建立知识库并整合有效特征构建、数据收集及模型选择的规则。随后，引入多粒度人类行为表示，并设计利用大语言模型生成策略的流程。通过盲法对比研究与可用性评估验证了该方法。最终，我们的方法有望彻底改变移动感知领域及其应用。