Towards an Adaptive Assistance System for Monitoring Tasks: Assessing Mental Workload using Eye-Tracking and Performance Measures

Buchholz, Victoria; Kopp, Stefan

doi:10.1109/ichms49158.2020.9209435

Cited by 6 publications

(2 citation statements)

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“…First, self-reported and performance inference [ 17 , 18 ] are the most widely used methods in ergonomics; these two methods are generally accepted and can be applied in most cases. Along with the recent development of smart sensors, some studies have used physiological measurement methods [ 3 , 17 , 19 , 20 ] in the field of applied ergonomics, which reflects the desire to obtain more objective and precise measures of cognitive workload.…”

Section: Introductionmentioning

confidence: 99%

“…A study conducted on operators in control rooms found that eye-tracking metrics and task performance are suitable for indicating mental workload levels in monitoring tasks. Specifically, a significant decrease in mean relative pupil size has been reported from difficult to medium and simple conditions [ 19 ]. In a study conducted on the role of pilots collaborating with unmanned aerial vehicles, the physiological features of ECG and eye-tracking (ET) sensors were applied in the designated scenarios.…”

Section: Introductionmentioning

confidence: 99%

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Determining Cognitive Workload Using Physiological Measurements: Pupillometry and Heart-Rate Variability

Ma,

Monfared,

Grant

et al. 2024

Sensors

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The adoption of Industry 4.0 technologies in manufacturing systems has accelerated in recent years, with a shift towards understanding operators’ well-being and resilience within the context of creating a human-centric manufacturing environment. In addition to measuring physical workload, monitoring operators’ cognitive workload is becoming a key element in maintaining a healthy and high-performing working environment in future digitalized manufacturing systems. The current approaches to the measurement of cognitive workload may be inadequate when human operators are faced with a series of new digitalized technologies, where their impact on operators’ mental workload and performance needs to be better understood. Therefore, a new method for measuring and determining the cognitive workload is required. Here, we propose a new method for determining cognitive-workload indices in a human-centric environment. The approach provides a method to define and verify the relationships between the factors of task complexity, cognitive workload, operators’ level of expertise, and indirectly, the operator performance level in a highly digitalized manufacturing environment. Our strategy is tested in a series of experiments where operators perform assembly tasks on a Wankel Engine block. The physiological signals from heart-rate variability and pupillometry bio-markers of 17 operators were captured and analysed using eye-tracking and electrocardiogram sensors. The experimental results demonstrate statistically significant differences in both cardiac and pupillometry-based cognitive load indices across the four task complexity levels (rest, low, medium, and high). Notably, these developed indices also provide better indications of cognitive load responding to changes in complexity compared to other measures. Additionally, while experts appear to exhibit lower cognitive loads across all complexity levels, further analysis is required to confirm statistically significant differences. In conclusion, the results from both measurement sensors are found to be compatible and in support of the proposed new approach. Our strategy should be useful for designing and optimizing workplace environments based on the cognitive load experienced by operators.

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