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Circadian rhythms influence multiple essential biological activitiesincluding sleep, performance, and mood. The dim light melatonin onset (DLMO) isthe gold standard for measuring human circadian phase (i.e., timing). Thecollection of DLMO is expensive and time-consuming since multiple saliva orblood samples are required overnight in special conditions, and the samplesmust then be assayed for melatonin. Recently, several computational approacheshave been designed for estimating DLMO. These methods collect daily sampleddata (e.g., sleep onset/offset times) or frequently sampled data (e.g., lightexposure/skin temperature/physical activity collected every minute) to trainlearning models for estimating DLMO. One limitation of these studies is thatthey only leverage one time-scale data. We propose a two-step framework forestimating DLMO using data from both time scales. The first step summarizesdata from before the current day, while the second step combines this summarywith frequently sampled data of the current day. We evaluate three movingaverage models that input sleep timing data as the first step and use recurrentneural network models as the second step. The results using data from 207undergraduates show that our two-step model with two time-scale features hasstatistically significantly lower root-mean-square errors than models that useeither daily sampled data or frequently sampled data.

Sensor-Based Estimation of Dim Light Melatonin Onset (DLMO) Using Features of Two Time Scales