Document Type

Dissertation

Degree

Doctor of Philosophy (PhD)

Major/Program

Electrical Engineering

First Advisor's Name

Ou Bai

First Advisor's Committee Title

Committee Chair

Second Advisor's Name

Armando Barreto

Second Advisor's Committee Title

Committee member

Third Advisor's Name

Gang Quan

Third Advisor's Committee Title

Committee member

Fourth Advisor's Name

Jean H. Andrian

Fourth Advisor's Committee Title

Committee member

Fifth Advisor's Name

Wei-Chiang Lin

Fifth Advisor's Committee Title

Committee member

Keywords

Functional Near-Infrared Spectroscopy (fNIRS), Inertia measurementunit, machine learning, motion artifacts removal, vigilance during walking, sensor location optimization, sensor fusion, arx modeling

Date of Defense

3-27-2020

Abstract

Human vigilance is a cognitive function that requires sustained attention toward change in the environment. Human vigilance detection is a widely investigated topic which can be accomplished by various approaches. Most studies have focused on stationary vigilance detection due to the high effect of interference such as motion artifacts which are prominent in common movements such as walking. Functional Near-Infrared Spectroscopy is a preferred modality in vigilance detection due to the safe nature, the low cost and ease of implementation. fNIRS is not immune to motion artifact interference, and therefore human vigilance detection performance would be severely degraded when studied during locomotion. Properly treating and removing walking-induced motion artifacts from the contaminated signals is crucial to ensure accurate vigilance detection. This study compared the vigilance level detection during both stationary and walking states and confirmed that the performance of vigilance level detection during walking is significantly deteriorated (with a p

Identifier

FIDC008952

ORCID

https://orcid.org/0000-0003-4149-6260

Previously Published In

[1] Siddiquee, M.R., Marquez, J.S., Atri, R., Ramon, R., Mayrand, R.P. and Bai, O., 2018. Movement artefact removal from NIRS signal using multi-channel IMU data. Biomedical engineering online, 17(1), pp.1-16.

[2] Siddiquee, M.R., Xue, T., Marquez, J.S., Atri, R., Ramon, R., Mayrand, R.P., Leung, C. and Bai, O., 2019, June. Sensor Fusion in Human Cyber Sensor System for Motion Artifact Removal from NIRS Signal. In 2019 12th International Conference on Human System Interaction (HSI) (pp. 192-196). IEEE.

[3] Siddiquee, M.R., Hasan, S.S., Marquez, J.S., Ramon, R.N. and Bai, O., 2020. Accurate Vigilance Detection During Gait by Using Movement Artifact Removal. IEEE Access, 8, pp.51179-51188.

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