GLOBEM EDA From Raw Sensors to Model Ready Decisions

This report presents a comprehensive exploratory data analysis (EDA) of the GLOBEM dataset to assess its suitability for personalized behavioural trajectory modelling. Using passive smartphone sensing, wearable device data, and mental health surveys collected across four annual cohorts (2018–2021), the analysis evaluates data quality, temporal coverage, missingness patterns, feature distributions, behavioural variability, cross-modal relationships, and cohort drift. The findings provide practical recommendations for designing transformer-based AI models that learn individual behavioural baselines and detect meaningful deviations over time, supporting future research in digital phenotyping and mental health monitoring.