TY - GEN
T1 - A lightweight and inexpensive in-ear sensing system for automatic whole-night sleep stage monitoring
AU - Nguyen, Anh
AU - Alqurashi, Raghda
AU - Raghebi, Zohreh
AU - Banaei-Kashani, Farnoush
AU - Halbower, Ann C.
AU - Vu, Tam
N1 - Publisher Copyright:
© 2016 Copyright held by the owner/author(s).
PY - 2016/11/14
Y1 - 2016/11/14
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85007107753&partnerID=8YFLogxK
U2 - 10.1145/2994551.2994562
DO - 10.1145/2994551.2994562
M3 - Conference contribution
AN - SCOPUS:85007107753
T3 - Proceedings of the 14th ACM Conference on Embedded Networked Sensor Systems, SenSys 2016
SP - 230
EP - 244
BT - Proceedings of the 14th ACM Conference on Embedded Networked Sensor Systems, SenSys 2016
PB - Association for Computing Machinery, Inc
T2 - 14th ACM Conference on Embedded Networked Sensor Systems, SenSys 2016
Y2 - 14 November 2016 through 16 November 2016
ER -