*Equal Contributors
Data from wearable sensors (e.g., heart rate, step count) can be used to model mood patterns. We characterize feature representations and modeling strategies with multi-modal discrete time series data for mood pattern classification with a large dataset with naturalistic missingness (n=116,819 participants) using 12 wearable data streams, with a focus on capturing periodic trends in data. Considering both performance and robustness, periodicity-based aggregate feature representations with gradient boosting models outperformed other representations and architectures…
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