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

Published:21 July 2021Publication History
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Abstract

Circadian rhythms influence multiple essential biological activities, including sleep, performance, and mood. The dim light melatonin onset (DLMO) is the gold standard for measuring human circadian phase (i.e., timing). The collection of DLMO is expensive and time consuming since multiple saliva or blood samples are required overnight in special conditions, and the samples must then be assayed for melatonin. Recently, several computational approaches have been designed for estimating DLMO. These methods collect daily sampled data (e.g., sleep onset/offset times) or frequently sampled data (e.g., light exposure/skin temperature/physical activity collected every minute) to train learning models for estimating DLMO. One limitation of these studies is that they only leverage one time-scale data. We propose a two-step framework for estimating DLMO using data from both time scales. The first step summarizes data from before the current day, whereas the second step combines this summary with frequently sampled data of the current day. We evaluate three moving average models that input sleep timing data as the first step and use recurrent neural network models as the second step. The results using data from 207 undergraduates show that our two-step model with two time-scale features has statistically significantly lower root-mean-square errors than models that use either daily sampled data or frequently sampled data.

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        • Published in

          cover image ACM Transactions on Computing for Healthcare
          ACM Transactions on Computing for Healthcare  Volume 2, Issue 3
          Survey Paper
          July 2021
          226 pages
          ISSN:2691-1957
          EISSN:2637-8051
          DOI:10.1145/3476113
          Issue’s Table of Contents

          Copyright © 2021 ACM

          Publisher

          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 21 July 2021
          • Accepted: 1 January 2021
          • Revised: 1 October 2020
          • Received: 1 January 2020
          Published in health Volume 2, Issue 3

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