Achieving Accurate Ubiquitous Sleep Sensing with Consumer Wearable Activity Wristbands Using Multi-class Imbalanced Classification

Consumer activity wristbands such as Fitbit provide an affordable method for ubiquitous sleep sensing in daily settings. These devices are also increasingly used in scientific studies as measurement tools of sleep outcomes. Nevertheless, the accuracy of Fitbit has raised wide concern. In this paper,...

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Bibliographic Details

Published in:	2019 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech) pp. 768 - 775
Main Authors:	Liang, Zilu, Chapa Martell, Mario Alberto
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01-08-2019
Subjects:	Classification algorithms data quality Fitbit Heart rate machine learning Machine learning algorithms Sleep Training Vegetation wearable
Online Access:	Get full text
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