Abstract

Electroencephalography (EEG) has been shown to be a reliable tool in neuroergonomics studies due to the relatively low cost of brain data collection and limited body invasion. The application of EEG frequency bands (including theta, alpha and beta), enjoyed a wide range of interest in physical and cognitive ergonomics. The psychophysical approach has been used for decades to improve safe work practices by understanding human limitations in manual materials handling. The main objective of this research project was to study the brain's EEG activity expressed by the power spectral density during manual lifting tasks related to: 1) the maximum acceptable weight of lift (MAWL) and 2) isokinetic and isometric lifting strength tests measurement outcomes. The first study investigated the changes in EEG power spectral density during determination of MAWL under low, medium, and high lifting frequencies. A high-density wireless dry cell EEG device has been used to record EEG signals. Twenty healthy males participated in this study. Subjects repeated the same experiment after two weeks. Analysis of variance (ANOVA) showed significant differences in EEG power spectral density between different lifting frequencies at three main brain areas (frontal, central, and parietal). The second study revealed differences in brain activities during isokinetic and isometric strength measurements, based on the recording and analysis of EEG power spectral density. This research project is the first study of EEG activity during manual lifting tasks, including the assessment of MAWL by the psychophysical method, as well as the measurement of human isokinetic and isometric strengths. The results of this project are considered critical to our increased understanding of the neural correlates of human physical activities, and consequently should have a positive impact on workplace design that considers brain activity related to specific human capabilities and limitations in manual lifting tasks.

Notes

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Graduation Date

2016

Semester

Spring

Advisor

Xanthopoulos, Petros

Degree

Doctor of Philosophy (Ph.D.)

College

College of Engineering and Computer Science

Department

Industrial Engineering and Management Systems

Degree Program

Industrial Engineering

Format

application/pdf

Identifier

CFE0006067

URL

http://purl.fcla.edu/fcla/etd/CFE0006067

Language

English

Release Date

May 2016

Length of Campus-only Access

None

Access Status

Doctoral Dissertation (Open Access)

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