Wearable technologies are investigated to provide in situ monitoring of manual material handling. New methodologies are presented which semi-automate existing biomechanical models for use with wearable technologies. Occupational health and safety is an important worldwide area of investment. This thesis undertakes an analysis of safe lifting for occupational environments, by focusing on modernising biomechanical models for low back injury risk factor estimations with wearable technology. The research topics explored in this thesis focused primarily on developing new methods of applying wearable inertial sensors for monitoring resistance exercise, supporting the application of wearables through validation analysis with popular manual methods and higher standard criterions. Will Hopkins Typical Error of the Estimate and Bland Altman Limit of Agreement analyses were implemented for validations. To aid in widespread adoption and standardisation, prior to any intervention, validation of new technologies and methods are required to support reliability, accuracy and practicality. A gap in literature was filled with no known previous validations that directly reported inertial sensors temporal errors during resistance exercises, mounted on the skin or for worn devices. Furthermore current ergonomic standards for primary prevention of low back disorders were reviewed and models for this purpose were identified, with greater importance given to those which may be monitored with wearables. After review the University of Utah Hand Calculated Back Compressive Force Equation and the Revised National Institute of Safety and Health Lifting Equation were chosen for analysis. New methodologies are presented which semi-automated these models, providing a foundation to support future research. This multidisciplinary research included a combination of biomechanics of resistance exercise, ergonomics of manual material handling and wearables engineering. Bridging the gap between disciplines created an emerging multidisciplinary niche which allowed diverse perspectives that may contribute to overcoming a growing health concern, specifically low back disorders, in the future.
|Date of Award||Jan 2019|
|Supervisor||Jim Lee (Supervisor), Daniel James (Supervisor) & Timothy Skinner (Supervisor)|
Validating new wearable technology methods to semi-automate biomechanical models for primary prevention of low back disorders in the workplace
Gleadhill, S. (Author). Jan 2019
Student thesis: Doctor of Philosophy (PhD) - CDU