A multi-modal device for application in microsleep detection release_kw3lhdqpezbj5bt3irfluwq5na

by Simon James Knopp, University Of Canterbury

Published by University of Canterbury. Electrical and Computer Engineering.

2015  

Abstract

Microsleeps and other lapses of responsiveness can have severe, or even fatal, consequences for people who must maintain high levels of attention on monotonous tasks for long periods of time, e.g., commercial vehicle drivers, pilots, and air-traffic controllers. This thesis describes a head-mounted system which is the first prototype in the process of creating a system that can detect (and possibly predict) these lapses in real time. The system consists of a wearable device which captures multiple physiological signals from the wearer and an extensible software framework for imple- menting signal processing algorithms. Proof-of-concept algorithms are implemented and used to demonstrate that the system can detect simulated microsleeps in real time. The device has three sensing modalities in order to get a better estimate of the user's cognitive state than by any one alone. Firstly, it has 16 channels of EEG (8 currently in use) captured by 24-bit ADCs sampling at 250 Hz. The EEG is acquired by custom-built dry electrodes consisting of spring-loaded, gold-plated pins. Secondly, the device has a miniature video camera mounted below one eye, providing 320 x 240 px greyscale video of the eye at 60 fps. The camera module includes infrared illumination so that it can operate in the dark. Thirdly, the device has a six-axis IMU to measure the orientation and movement of the head. These sensors are connected to a Gumstix computer-on-module which transmits the captured data to a remote computer via Wi-Fi. The device has a battery life of about 7.4 h. In addition to this hardware, software to receive and analyse data from the head-mounted device was developed. The software is built around a signal processing pipeline that has been designed to encapsulate a wide variety of signal processing algorithms; feature extractors calculate salient properties of the input data and a classifier fuses these features to determine the user's cognitive state. A plug-in system is provided which allows users to write their own signal processi [...]
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