Poster Abstract: Fine-grained Contextualized Activity Logs Generation based on Multi-Modal Sensor Data and LLM

Detailed activity logs are crucial for health monitoring and personalized interventions. Traditional methods rely on manual editing or raise privacy concerns due to the use of camera recordings. This paper proposes ContextLLM, an innovative system that utilizes a large language model (LLM) to understand sensor data from smartphones and smartwatches and automatically generate contextualized activity logs. Compared to the state-of-the-art, it incorporates key contextual information and physiological indicators, enabling more fine-grained semantic descriptions. Preliminary results show that the automatically generated activity logs achieve 80.26% similarity to human annotations, demonstrating the feasibility.