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Articles

Effects of augmented reality glasses on the cognitive load of assembly operators in the automotive industry

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Pages 487-499 | Received 20 Mar 2020, Accepted 01 Mar 2021, Published online: 21 Mar 2021
 

ABSTRACT

Recently, new digital technologies are frequently used in the industry within the scope of Industry 4.0. Augmented reality (AR) is one of these emerging technologies; it assists users with the help of virtual images on the real display of the work environment. Although AR benefits employees in terms of productivity and quality, it is not precisely known if it has a cognitive effect on the employees. In this study, the effect of AR glasses on the cognitive loads of the employees was investigated. The experiments were carried out on the assembly line of an automobile company. Electroencephalography (EEG) and NASA Task Load Index (NASA-TLX) were used to measure cognitive load. The current and AR-based work procedures were examined comparatively. Also, the difference between the cognitive load on the first and last days of experiments was compared to determine the adaptation status of the employees. The average of the area under the EEG graph as a measure of cognitive load, were 18.96 and 5.24 μV*ms in employees working with and without AR glasses, respectively. NASA-TLX scores of the employees decreased by 10% when they work with AR glasses. So, there was a less cognitive load on employees in the case of using AR glasses. And there was no significant difference between the first and last days of device usage. Hence, AR glasses did not constitute an extra cognitive load from the first day of usage.

Acknowledgments

This study is an output of the TUBITAK project “An investigation of cognitive load effect of using augmented reality glasses in automotive manufacturing”, project number 217M505.

The authors thank Tofaş Turkish Automobile Factory Inc. for their contributions.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the Scientific and Technological Research Council of Turkey, with project number [217M505].

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