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

Autor:	Kolbeinsson, Arinbjörn, Gade, Piyusha, Kainkaryam, Raghu, Jankovic, Filip, Foschini, Luca
Rok vydání:	2021
Předmět:	Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: The workshop on Self-Supervised Learning at NeurIPS (2021)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2112.13755 Zobrazit plný text záznamu View this record from Arxiv