| Author: | Long, Yitian |
| Title: | The attention prediction and optimization of operation interface in a tower crane VR simulation training |
| Advisors: | Chi, Hung Lin (BRE) |
| Degree: | M.Sc. |
| Year: | 2024 |
| Subject: | Construction equipment operators Virtual reality Tower cranes -- Computer simulation Construction workers -- Training of Human engineering Hong Kong Polytechnic University -- Dissertations |
| Department: | Department of Building and Real Estate |
| Pages: | 58 pages : color illustrations |
| Language: | English |
| Abstract: | In the case of large-scale construction equipment, such as tower cranes, where a high level of concentration is required from the operator, the operator has to be sure that he/she is able to allocate his/her attention to different tasks in order to complete them efficiently in a safe manner. The use of VR equipment for tower crane operation simulation training can be a low-cost and effective way to train operators in the relevant operations. One of the problems is how to design the operation interface in such a simulation so that it can better provide different information according to different operation tasks. Salience, effort, expectancy, value (SEEV) model is a model that can predict attention distribution and its predictive ability has been comprehensively established in areas such as driving and surgery. However, the effectiveness of the SEEV model in predicting attention distribution in a tower crane simulation training operation interface has not been thoroughly investigated. This study's primary goals are to verify the SEEV model's viability for operation attention allocation prediction in the context of tower crane operations and to use the model to optimise the current operational interface. Regarding the quantification of the four factors, this study used a saliency heat-map, a distance measurement tool, the System Usability Scale (SUS) and the lowest-order algorithm, respectively. A prediction model of attention in tower crane simulation operation training was constructed. To validate the accuracy of the prediction model, a total of 43 individuals were enlisted in this study and were assigned to perform a simulated operation of a tower crane hoisting task on a motion platform by using a VR device and their attention distribution on the operation interface was recorded during the process by the eye-tracking function in the VR glasses. The experiment participants operated and controlled the tower crane in the simulated scenario to perform the lifting, moving and placing tasks according to the experiment requirements. The findings indicated a strong linear relationship between the projected data derived from the SEEV model and the eye-tracking data acquired during the experiment. This experiment validates the possibility of the SEEV model for the optimization of the operation interface in the tower crane training simulation scenarios. Valid conclusions are provided for future related experiments and designs. |
| Rights: | All rights reserved |
| Access: | restricted access |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 7611.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 9.24 MB | Adobe PDF | View/Open |
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