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 language | English (US) |
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Title of host publication | Proceedings of the 14th ACM Conference on Embedded Networked Sensor Systems, SenSys 2016 |
Publisher | Association for Computing Machinery, Inc |
Pages | 230-244 |
Number of pages | 15 |
ISBN (Electronic) | 9781450342636 |
DOIs | |
State | Published - Nov 14 2016 |
Externally published | Yes |
Event | 14th ACM Conference on Embedded Networked Sensor Systems, SenSys 2016 - Stanford, United States Duration: Nov 14 2016 → Nov 16 2016 |
Other
Other | 14th ACM Conference on Embedded Networked Sensor Systems, SenSys 2016 |
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Country/Territory | United States |
City | Stanford |
Period | 11/14/16 → 11/16/16 |
ASJC Scopus subject areas
- Hardware and Architecture
- Computer Networks and Communications
- Control and Systems Engineering
- Electrical and Electronic Engineering