Key result
A 1D-CNN improves multisensory platform software to achieve ~95% accuracy in detecting two stress levels.
Why the study?
Existing stress-monitoring platforms had limited accuracy in detecting multiple stress levels, and traditional psychological questionnaires lack real-time or continuous monitoring capabilities.
Does a 1D-convolutional neural network improve the accuracy of stress detection using minimally intrusive multisensory devices in workers?
Does a 1D-convolutional neural network improve the accuracy of stress detection using minimally intrusive multisensory devices in workers?
A 1D-convolutional neural network significantly improved the accuracy of a minimally intrusive wearable and ambient platform for detecting workers' stress levels.
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May support sensor-based stress monitoring in Industry 4.0; leaves open validation before any practice change.
Rescio et al. (2024) studied Work-related stress. 1D-convolutional neural network vs. Other neural networks / previous system was evaluated on Identification of two levels of stress. A 1D-convolutional neural network improved the software performance of a minimally intrusive multisensory platform, achieving 95.38% accuracy for identifying two levels of stress.
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