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Multimodal Assessment of Cognitive Workload Using Neural, Subjective and Behavioural Measures in Smart Factory
Zohreh Zakeri1, Arshia Arif1, Ahmet Omurtag1
1Department of Engineering, School of Science and Technology, Nottingham Trent University, Clifton, Nottingham NG11 8NS, UK.
Sensors (Basel, Switzerland)
|November 14, 2023
Summary
This study assessed factory workers' mental stress during human-robot collaboration using physiological and behavioral measures. Physiological data, like brain signals, can accurately predict stress and potentially replace traditional methods for better worker performance.
Area of Science:
- Human-Robot Interaction
- Industrial Ergonomics
- Cognitive Stress Measurement
Background:
- Industry 5.0 integrates humans and collaborative robots (cobots) in manufacturing.
- Human-robot interaction raises concerns about human factors and ergonomics.
- Cobots' unpredictability can cause cognitive stress, impacting worker productivity.
Purpose of the Study:
- To measure mental workload and stress in factory workers during human-robot collaboration.
- To investigate the impact of task complexity, cobot speed, and payload on worker stress.
- To explore the correlation between physiological, behavioral, and subjective stress measures.
Main Methods:
- Collected electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) for brain and hemodynamic activity.
- Assessed mental workload using physiological, behavioral, and subjective measures.
- Applied regression, artificial neural networks (ANN), and k-nearest neighbors (KNN) for data analysis.
Main Results:
- Task complexity and cobot speed significantly impacted worker mental stress.
- Regression analysis showed the best correlation (rsq-adj = 0.654146) for predicting missed beeps using EEG and fNIRS.
- KNN achieved 77.8% accuracy in correlating physiological variables with missed beeps.
Conclusions:
- Physiological measures (EEG, fNIRS) offer insightful, unbiased stress assessment in human-robot collaboration.
- These measures show potential to replace traditional, potentially biased, stress indicators.
- Optimizing human-robot interaction through stress monitoring can enhance worker performance and well-being.
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