Self-supervision of wearable sensors time-series data for influenza detection

• URL: http://arxiv.org/abs/2112.13755v1
• Date: Mon, 27 Dec 2021 16:09:43 GMT
• Title: Self-supervision of wearable sensors time-series data for influenza detection
• Authors: Arinbj\"orn Kolbeinsson, Piyusha Gade, Raghu Kainkaryam, Filip Jankovic, Luca Foschini
• Abstract summary: We show that using self-supervised learning to predict next-day time-series values allows us to learn rich representations which can be adapted to perform accurate ILI prediction. Our results show that predicting the next day's resting heart rate or time-in-bed during sleep provides better representations for ILI prediction.
• Score: 4.850820365312369
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Self-supervision may boost model performance in downstream tasks. However, there is no principled way of selecting the self-supervised objectives that yield the most adaptable models. Here, we study this problem on daily time-series data generated from wearable sensors used to detect onset of influenza-like illness (ILI). We first show that using self-supervised learning to predict next-day time-series values allows us to learn rich representations which can be adapted to perform accurate ILI prediction. Second, we perform an empirical analysis of three different self-supervised objectives to assess their adaptability to ILI prediction. Our results show that predicting the next day's resting heart rate or time-in-bed during sleep provides better representations for ILI prediction. These findings add to previous work demonstrating the practical application of self-supervised learning from activity data to improve health predictions.

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