Year

2020

Degree Name

Doctor of Philosophy

Department

School of Electrical, Computer and Telecommunications Engineering

Abstract

Operators and drivers of heavy vehicles that are a source of vibrations are at an elevated risk due the prolonged exposure to whole body vibrations. The pathological effects of whole-body vibrations are well defined in the literature and include the following: lower back pain, fatigue and degenerative disorders. The modelling and measurement of the biomechanical response of the seated human body is extensively used in the areas of ergonomics and automatic automotive suspension control system technologies.

In recent times there have been rapid advancements in wearable sensor technologies in terms of the size, weight, power, connectivity and data bandwidth. Microelectromechanical systems are capable of sensing inertial motion data, have become increasingly miniaturised. This allows for the practice noninvasive sampling inertial motion data and applying whole body vibration modelling to a broader scope.

Currently, there are two main gaps in the current state of the art research on whole body vibration modelling that this thesis aims to fill. The first is that many studies do not deal with the issue of real time modelling whole body vibration using streaming data. The second is the lack of use of machine learnt pattern-based methods for forecasting body model parameters.

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Unless otherwise indicated, the views expressed in this thesis are those of the author and do not necessarily represent the views of the University of Wollongong.