Skip to main navigation Skip to search Skip to main content

A lightweight and inexpensive in-ear sensing system for automatic whole-night sleep stage monitoring

  • Anh Nguyen
  • , Raghda Alqurashi
  • , Zohreh Raghebi
  • , Farnoush Banaei-Kashani
  • , Ann C. Halbower
  • , Tam Vu
  • University of Colorado Denver
  • University of Colorado Boulder

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

61 Scopus citations

Abstract

This paper introduces LIBS, a light-weight and inexpensive wearable sensing system, that can capture electrical activities of human brain, eyes, and facial muscles with two pairs of custom-built flexible electrodes each of which is embedded on an off-The-shelf foam earplug. A supervised nonnegative matrix factorization algorithm to adaptively analyze and extract these bioelectrical signals from a single mixed in-ear channel collected by the sensor is also proposed. While LIBS can enable a wide class of low-cost selfcare, human computer interaction, and health monitoring applications, we demonstrate its medical potential by developing an autonomous whole-night sleep staging system utilizing LIBS's outputs. We constructed a hardware prototype from off-The-shelf electronic components and used it to conduct 38 hours of sleep studies on 8 participants over a period of 30 days. Our evaluation results show that LIBS can monitor biosignals representing brain activities, eye movements, and muscle contractions with excellent fidelity such that it can be used for sleep stage classification with an average of more than 95% accuracy.

Original languageEnglish
Title of host publicationProceedings of the 14th ACM Conference on Embedded Networked Sensor Systems, SenSys 2016
PublisherAssociation for Computing Machinery
Pages230-244
Number of pages15
ISBN (Electronic)9781450342636
DOIs
StatePublished - Nov 14 2016
Event14th ACM Conference on Embedded Networked Sensor Systems, SenSys 2016 - Stanford, United States
Duration: Nov 14 2016Nov 16 2016

Publication series

NameProceedings of the 14th ACM Conference on Embedded Networked Sensor Systems, SenSys 2016

Conference

Conference14th ACM Conference on Embedded Networked Sensor Systems, SenSys 2016
Country/TerritoryUnited States
CityStanford
Period11/14/1611/16/16

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Fingerprint

Dive into the research topics of 'A lightweight and inexpensive in-ear sensing system for automatic whole-night sleep stage monitoring'. Together they form a unique fingerprint.

Cite this