Augmented Reality Device Operator Cognitive Strain Determination and Prediction

Patrick Seeling; Patrick Seeling

doi:10.3934/ElectrEng.2017.1.100

AIMS Electronics and Electrical Engineering

2017, Volume 1, Issue 1: 100-110. doi: 10.3934/ElectrEng.2017.1.100

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Research article Special Issues

Augmented Reality Device Operator Cognitive Strain Determination and Prediction

Patrick Seeling ^,

Department of Computer Science, Central Michigan University, Pearce Hall 413, Mount Pleasant, MI 48859, USA

Received: 28 September 2017 Accepted: 06 December 2017 Published: 13 December 2017

Augmented reality (AR) solutions are in the process of entering a broad variety of industries to modify the capabilities of workers through (close to) real-time display of context-dependent information. An example for real-time training is the display of instructional materials, such as manuals, for operation and maintenance. Especially in industrial settings, this will allow for the enhancement of workforce capabilities in real-time. However, little is known with respect to the cognitive load that is incurred as a result of this process, which might hinder the realization of desired outcomes. In this paper, we evaluate visual tasks with respect to the cognitive load based on electroencephalography (EEG) employing existing and new metrics utilizing a publicly available data set. In turn, we provide an initial quantified and directly measured approach. We find that overall results are highly subjective, but already available commercial equipment can readily be employed to determine the cognitive load with R² scores around 0.5 when utilizing k-nearest-neighbor (KNN) approaches directly. More intricate metrics at different measurement points could thus help detect and alleviate undesired stressors in industrial augmented reality settings.
- augmented reality,
- multimedia systems,
- electroencephalography,
- cognitive load,
- k-nearest-neighbor
Citation: Patrick Seeling. Augmented Reality Device Operator Cognitive Strain Determination and Prediction[J]. AIMS Electronics and Electrical Engineering, 2017, 1(1): 100-110. doi: 10.3934/ElectrEng.2017.1.100

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Abstract

Augmented reality (AR) solutions are in the process of entering a broad variety of industries to modify the capabilities of workers through (close to) real-time display of context-dependent information. An example for real-time training is the display of instructional materials, such as manuals, for operation and maintenance. Especially in industrial settings, this will allow for the enhancement of workforce capabilities in real-time. However, little is known with respect to the cognitive load that is incurred as a result of this process, which might hinder the realization of desired outcomes. In this paper, we evaluate visual tasks with respect to the cognitive load based on electroencephalography (EEG) employing existing and new metrics utilizing a publicly available data set. In turn, we provide an initial quantified and directly measured approach. We find that overall results are highly subjective, but already available commercial equipment can readily be employed to determine the cognitive load with R² scores around 0.5 when utilizing k-nearest-neighbor (KNN) approaches directly. More intricate metrics at different measurement points could thus help detect and alleviate undesired stressors in industrial augmented reality settings.

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