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|Title:||Measuring differences in eye glance behaviour for heavy equipment operators while driving in naturalistic worksite environments|
|Keywords:||Eye-Tracking;Occupational Health and Safety;Heavy Equipment;Construction;Accident Prevention|
|Abstract:||Struck-by accidents continue to be a predominant issue with catastrophic consequences in both construction and mining industries. These incidents are found to be largely caused by blind spots due to the equipment or environment, along with human error. Blind spots are considered to be non-visible areas where operator line-of-sight is impeded by parts of the heavy equipment, buildings or other objects in their environment. Eye-tracking (ET) research has mostly focused on the eye behaviour while operating passenger vehicles. This research includes, but is not limited to, eye scanning patterns of drivers, development and implementation vehicle interaction devices for safe use and enhanced education through training programs. Visual scanning patterns of heavy equipment operators cannot be lumped into that of those driving passenger vehicles as the design of various machinery is vastly different in addition to differences of driving on a public road compared to a worksite environment. The intention of this research was to initiate research specifically for ET data while operating various types of heavy equipment. The purpose of this research was to quantify typical heavy equipment operator eye gaze behaviour in natural worksite settings in both construction and mining industries. First, the objective was to determine the similarities on where operators naturally look while driving in a field setting. Key areas of interest (AOI) operators looked were identified during forward and rearward movements. The most noticeable difference was that the front view ahead was fixated the most while driving forwards, whereas the mirrors were tended to the majority of rearward movements basically never looking within the front views. As some heavy equipment, such as a load-haul-dump (LHD) have very unique characteristics including operator seated position within the machine, it was important to measure their visual scanning pattern while driving, especially in an underground mine. Therefore, the second objective was to obtain data to quantify typical LHD operator eye gaze behaviour in addition to determining differences between novice and expert operators. This was accomplished by comparing novice operator completing a four-day training program on an LHD simulator to a similar trial completed by the expert operator. Results demonstrated that visual scanning patterns of the novice operator was considerably more dispersed than that of the expert which was much more focal. However, it should be noted that the scanning pattern of novice operators did become more focal and less dispersed at the end of training, starting to resemble that of the expert. Other main findings revealed that regardless of direction travelled, operators spend the majority of time fixating within the central view ahead. Findings suggest that novice operators experienced an overall decrease in cognitive workload and relied less on using the edge of machine and wall as sightlines to monitor LHD speed and position within the mine as experience increased. Interestingly enough it appears that the expert operator utilized the right wall as a sightline while driving forwards, differing from novice operators. Determining the gaze behaviour of heavy equipment operators on various machines is important to have a baseline typical eye gaze behaviour to be able to detect deviations that can provide a measure of how technologies, policies or training programs affect gaze behaviour. Eye gaze behaviour needs to be incorporated in the design of future technologies (ex. proximity detection and awareness technology) to provide modifications to reduce cognitive workload and distraction that can occur when implementing devices. Findings of this research could also aid to enhance current training programs and advise the development of new training programs or policies to minimize human-equipment interactions.|
|Appears in Collections:||Human Kinetics - Master's Theses|
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|a.brunton_MHKThesis_Final_2020_09_11.pdf||6.29 MB||Adobe PDF|
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